Method for providing diagnostic auxiliary information and device for executing the same

Abstract

To provide a method for providing diagnostic aid information by analyzing a medical image, and a system for executing the same.SOLUTION: A provision method of diagnostic aid information for providing diagnostic aid information by analyzing a medical image, includes: acquiring an image; labeling a feature value reflecting a region of a brain; determining a reference boundary in the brain image; calculating a first disease index and a second disease index; and providing diagnostic aid information based on the first disease index and the second disease index.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a method for analyzing medical images to provide auxiliary diagnostic information and a system for implementing the method, and more particularly to a method for segmenting a medical image into multiple regions and providing auxiliary diagnostic information based on the relationships between the multiple regions and a system for implementing the method. [Background technology]

[0002] In the field of modern medical technology, there is an increasing demand for various techniques to precisely analyze medical images and provide more accurate disease-related information. In this trend, next-generation medical technology that analyzes medical images and provides a unified disease index is gaining attention.

[0003] There are many published methods for calculating indexes to provide disease-related information by analyzing medical images. However, most of these methods involve the clinical judgment of the doctor analyzing the medical images, and the disease-related information provided to patients can be overly subjective and subject to significant deviations depending on the doctor.

[0004] Furthermore, even when image segmentation is performed to provide disease-related information from medical images, the areas that can be segmented within the medical image vary depending on the conditions under which the medical image was acquired. As a result, it is difficult to obtain sufficient information about a disease within a specific medical image, and in order to provide accurate diagnostic support information, it is necessary to perform segmentation on each medical image acquired under various conditions, which is a hassle. Summary of the Invention [Problem to be solved by the invention]

[0005] The problem to be solved by the present invention is to provide a standardized standard for analyzing medical images and calculating disease indexes, thereby eliminating the influence of subjective judgment by doctors.

[0006] Another problem to be solved by the present invention is to calculate an objective and standardized disease index by analyzing medical images using a highly trained artificial neural network.

[0007] Another problem to be solved by the present invention is to provide disease information from which the influence of the diversity of medical images is removed by calculating a disease index corresponding to the properties of the medical images.

[0008] Another problem to be solved by the present invention is to increase the diversity of disease-related information provided by obtaining segmentation results having at least two or more unique characteristics from one medical image. [Means for solving the problem]

[0009] An apparatus for providing diagnostic auxiliary information according to an embodiment of the present invention includes a communication module that acquires MRI images related to the brain, a memory that stores a program for analyzing the images, and a controller that analyzes the MRI images related to the brain using the program stored in the memory. The controller distinguishes between ventricular regions and white matter hyperintensity signal regions from the images, sets a reference boundary a predetermined distance away from the ventricular regions, calculates a first disease index based on white matter hyperintensity signal regions located within the reference boundary, and a second disease index based on white matter hyperintensity signal regions located outside the reference boundary, and provides diagnostic auxiliary information based on the first disease index and the second disease index.

[0010] According to another embodiment of the present invention, a method for providing diagnostic auxiliary information can provide brain-related diagnostic auxiliary information, the method comprising the steps of: acquiring a brain image including a plurality of cells; labeling the plurality of cells with feature values ​​reflecting brain regions, where the brain regions include ventricles and white matter hyperintensities; determining a reference boundary within the brain image, where the reference boundary is defined as a set of cells located a predetermined distance away from the ventricles; calculating a first disease index associated with cells labeled with feature values ​​representing white matter hyperintensities located within the reference boundary and a second disease index associated with cells labeled with feature values ​​representing white matter hyperintensities located outside the reference boundary; and providing the diagnostic auxiliary information based on the first disease index and the second disease index. [Effects of the Invention]

[0011] According to the present invention, by providing a standardized standard when calculating a disease index from a medical image, it is possible to provide objective and clear disease information from the patient's condition.

[0012] According to the present invention, a method for calculating a criterion that reflects characteristics contained in a medical image is provided, thereby providing a method for providing disease information that can be applied to various medical images.

[0013] According to the present invention, by analyzing medical images using a highly trained artificial neural network, objective and clear disease information can be provided, eliminating the influence of a doctor's clinical judgment. According to the present invention, by utilizing an artificial neural network trained through medical images with at least two or more different characteristics, it is possible to accurately provide disease information for medical images that correspond to the characteristics of the patient's disease. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram illustrating an example of a system for providing auxiliary diagnostic information according to an embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing a schematic configuration of a system for providing auxiliary diagnostic information according to an embodiment of the present invention. [Figure 3] FIG. 3 is a block diagram showing the configuration of an image capturing device according to an embodiment of the present invention. [Figure 4] FIG. 4 is a block diagram showing the configuration of a video analysis device according to an embodiment of the present invention. [Figure 5] FIG. 5 is a general flowchart illustrating a method for providing auxiliary diagnostic information executed in a system for providing auxiliary diagnostic information according to an embodiment of the present invention. [Figure 6] FIG. 6 is a diagram illustrating a medical image according to an embodiment of the present invention. [Figure 7] FIG. 7 illustrates exemplary medical images according to various acquisition conditions in accordance with an embodiment of the present invention. [Figure 8] FIG. 8 is a flowchart illustrating a detailed operation of a medical image analysis performed by the image analysis apparatus according to an embodiment of the present invention. [Figure 9] FIG. 9 is a diagram illustrating a segmented medical image according to an embodiment of the present invention. [Figure 10] FIG. 10 is a flowchart illustrating an example method for extracting disease indexes from medical images according to an embodiment of the present invention. [Figure 11] FIG. 11 is a diagram illustrating an example of a medical image showing a reference region and a reference boundary according to an embodiment of the present invention. [Figure 12] FIG. 12 is a diagram illustrating an example of a process for obtaining region information of a target region according to an embodiment of the present invention. [Figure 13] FIG. 13 is a diagram illustrating another example of a process for obtaining region information of a target region according to an embodiment of the present invention. [Figure 14] FIG. 14 is a diagram illustrating the provision of diagnostic assistance information obtained based on a disease index calculated according to an embodiment of the present invention. [Figure 15] FIG. 15 is a flowchart illustrating an example of a method for providing auxiliary diagnostic information using a reference boundary modified according to an embodiment of the present invention. [Figure 16] FIG. 16 is a diagram illustrating an overlap of a reference boundary and a region of non-interest according to an embodiment of the present invention. [Figure 17a] FIG. 17a is an exemplary diagram of a method for calculating a modified reference boundary according to an embodiment of the present invention. [Figure 17b] FIG. 17b is an exemplary diagram of a method for calculating a modified reference boundary according to an embodiment of the present invention. [Figure 18] FIG. 18 is an exemplary diagram of a reference boundary that has been partially modified and a reference boundary that has been entirely modified according to an embodiment of the present invention. [Figure 19] FIG. 19 is a diagram illustrating a specific modification process of a partially modified reference boundary according to an embodiment of the present invention. [Figure 20] FIG. 20 is a schematic flow chart illustrating a method for an image analyzer to provide auxiliary diagnostic information from a plurality of medical images according to an embodiment of the present invention. [Figure 21] FIG. 21 is a diagram illustrating an example of multiple medical images according to an embodiment of the present invention. [Figure 22] FIG. 22 is a diagram illustrating an example of slice images each containing other information and the provision of diagnostic auxiliary information therefrom according to an embodiment of the present invention. [Figure 23] FIG. 23 is a flow chart that schematically illustrates a method for obtaining auxiliary diagnostic information from a candidate image according to an embodiment of the present invention. [Figure 24] FIG. 24 is a diagram illustrating candidate images according to an embodiment of the present invention. [Figure 25] FIG. 25 is a diagram illustrating an example of providing auxiliary diagnostic information from a candidate image according to an embodiment of the present invention. [Figure 26] FIG. 26 is a diagram illustrating another example of providing auxiliary diagnostic information according to an embodiment of the present invention. [Figure 27] FIG. 27 is a flowchart illustrating a method for obtaining a disease index based on multiple images according to an embodiment of the present invention. [Figure 28] FIG. 28 is a diagram illustrating an example of a process for calculating a disease index of a second image by taking into account information of a first image according to an embodiment of the present invention. [Figure 29] FIG. 29 is a diagram illustrating another example of a process of calculating a disease index of a second image by taking into account information of a first image according to an embodiment of the present invention. [Figure 30] FIG. 30 is a flowchart illustrating an example of a method for providing diagnostic auxiliary information based on a 3D medical model according to an embodiment of the present invention. [Figure 31] FIG. 31 is a diagram illustrating an example of a process for calculating a disease index from a 3D medical model according to an embodiment of the present invention. [Figure 32] FIG. 32 is a diagram illustrating another example of a process for calculating a disease index from a 3D medical model according to an embodiment of the present invention. [Figure 33] FIG. 33 is a schematic diagram illustrating a medical image segmentation operation performed by a video analysis device according to an embodiment of the present invention. [Figure 34] FIG. 34 illustrates an example of an artificial neural network model according to an embodiment of the present invention. [Figure 35] FIG. 35 illustrates another implementation of an artificial neural network model according to an embodiment of the present invention. [Figure 36] FIG. 36 illustrates an example of a segmentation result using an artificial neural network model according to an embodiment of the present invention. [Figure 37] FIG. 37 is an illustration of a medical image segmented to include multiple features according to an embodiment of the present invention. [Figure 38]FIG. 38 is a flowchart illustrating the training process of an artificial neural network according to an embodiment of the present invention. [Figure 39] FIG. 39 is an example showing a process of matching a first image with a second image according to an embodiment of the present invention. [Figure 40] FIG. 40 illustrates an example of morphological modification according to an embodiment of the present invention. [Figure 41] FIG. 41 is a flowchart illustrating a deploying process using an artificial neural network of a video analysis device according to an embodiment of the present invention. [Figure 42] FIG. 42 shows an example of the final output result of the artificial neural network according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] An apparatus for providing diagnostic auxiliary information according to an embodiment of the present invention includes a communication module for acquiring MRI images related to the brain, a memory storing a program for analyzing the images, and a controller for analyzing the MRI images related to the brain using the program stored in the memory. The controller distinguishes between a ventricular region and a white matter hyperintensity signal region from the images, sets a reference boundary a predetermined distance away from the ventricular region, calculates a first disease index based on the white matter hyperintensity signal region present within the reference boundary, and a second disease index based on the white matter hyperintensity signal region present outside the reference boundary, and provides diagnostic auxiliary information based on the first disease index and the second disease index.

[0016] According to another embodiment of the present invention, a method for providing diagnostic auxiliary information can provide brain-related diagnostic auxiliary information, the method comprising the steps of: acquiring a brain image including a plurality of cells; labeling the plurality of cells with feature values ​​reflecting brain regions, where the brain regions include ventricles and white matter hyperintensities; determining a reference boundary within the brain image, where the reference boundary is defined as a set of cells located a predetermined distance away from the ventricles; calculating a first disease index associated with cells labeled with feature values ​​representing white matter hyperintensities located within the reference boundary and a second disease index associated with cells labeled with feature values ​​representing white matter hyperintensities located outside the reference boundary; and providing diagnostic auxiliary information based on the first disease index and the second disease index.

[0017] The above-mentioned objects, features, and advantages of the present invention will become more apparent from the following detailed description of the accompanying drawings. Of course, the present invention can be realized in various modified embodiments, but the following specific embodiments will be illustrated and described in detail as examples.

[0018] In the drawings, the thicknesses of layers and regions are exaggerated for clarity, and a description of an element or layer being "on" or "above" another element or layer includes not only the case where the element or layer is located immediately above the other element or layer, but also the case where other layers or other elements are interposed therebetween. In this specification, the same reference numerals generally refer to the same elements. In addition, in the drawings relating to each embodiment, elements having the same function within the same conceptual scope are described using the same reference numerals.

[0019] If a detailed description of well-known functions or configurations related to the present invention is deemed unnecessary for the gist of the present invention, the detailed description will be omitted. Furthermore, numbers (e.g., 1, 2, etc.) used in the description of this specification are merely identification symbols for distinguishing one component from another.

[0020] In the following description, the terms "module" and "section" which represent the units of components are used for the ease of writing specifications, and the terms themselves do not have any distinguishing meaning or role.

[0021] SUMMARY OF THE INVENTION The present disclosure relates to a method for analyzing medical images to provide diagnostic aids related to the medical images, and a system for implementing the method.

[0022] Here, the term "medical image" in this specification can be interpreted to include various images that can be acquired in the medical industry. That is, the term "medical image" in this specification is a general term for images that can be acquired from various devices used in the medical industry. One example is an image acquired by a computer tomography (CT) device such as a magnetic resonance imaging (MRI). Another example is a medical image acquired by an X-ray device. Of course, medical images are not limited to these images and can include all images that can be acquired in the medical industry, such as photographs that can be acquired by a general camera device.

[0023] Furthermore, the target object of a medical image according to one embodiment may include all photographed objects in the medical industry. For example, the medical image may be an image of a patient. Specifically, the medical image may be an image of a specific part of the patient's body. As a more specific example, the medical image may be a photograph related to the brain of a patient suspected of dementia, or an image related to the lungs of a patient suspected of lung cancer. In addition, the target object of a medical image according to another embodiment may be bodily tissue extracted from a human.

[0024] In other words, the medical image in this specification may refer to any image of an object in the medical industry from which disease information is to be obtained using an image analysis method according to the concept of the present invention, or may refer to an image having a form obtained by various devices used in the medical industry. However, for the sake of convenience, the following description of this specification will mainly focus on magnetic resonance imaging (MRI) of the human brain, but the concept of the present invention is not limited thereto.

[0025] Furthermore, in this specification, "auxiliary diagnostic information" refers to comprehensive information that can be determined from a medical image and that can be used to objectively determine a disease. As an example, the auxiliary diagnostic information in this specification is the presence or absence of a specific disease. As another example, the auxiliary diagnostic information is the progression of a specific disease. As another example, the auxiliary diagnostic information is the severity of a disease. As yet another example, the auxiliary diagnostic information is a numerical representation of the degree of disease in a specific patient compared to the average population. In addition, in this specification, all information related to a disease that can be determined within a medical image, as described above, can be collectively referred to as "auxiliary diagnostic information."

[0026] First, the configuration of a diagnostic auxiliary information providing system according to one embodiment and an example of its execution will be described with reference to FIGS. 1 and 2. FIG.

[0027] FIG. 1 shows an example of an implementation of a diagnostic auxiliary information providing system according to an embodiment of the present invention, and FIG. 2 is a block diagram showing a schematic configuration of the diagnostic auxiliary information providing system according to an embodiment of the present invention.

[0028] Referring to FIGS. 1 and 2, a system (10000) according to an embodiment can analyze medical images and provide auxiliary diagnostic information.

[0029] In this embodiment, the system (10000) includes an image capture device (1000) and an image analysis device (2000).

[0030] The image acquisition device 1000 can acquire medical images from an object to be imaged. Here, the image acquisition device 1000 refers to various devices or systems capable of acquiring medical images. For example, as shown in FIG. 1, the image acquisition device 1000 is an MRI device. However, as described above, the image acquisition device 1000 referred to in this specification is not limited to an MRI device.

[0031] The image analysis device 2000 can provide auxiliary diagnostic information by analyzing medical images acquired from the image acquisition device 1000. Specifically, the image analysis device 2000 can extract a number of disease-related indices from the medical images and calculate auxiliary diagnostic information based on the extracted indices. The medical image analysis operation performed by the image analysis device 2000 will be described in detail below.

[0032] Also, although the image capturing device 1000 and the image analyzing device 2000 have been described above as separate devices, this is merely an example and includes various forms that can be implemented as the diagnostic assistance information providing system 10000. That is, the image analyzing device 2000 according to the embodiment can be implemented as a separate server, or can be implemented as a program integrated into the image capturing device 1000. However, for the sake of convenience, the present specification will describe the system 10000 implemented as a separate image analyzing device 2000.

[0033] Hereinafter, configurations of an image capturing device and an image analyzing device according to an embodiment will be described with reference to the accompanying drawings.

[0034] FIG. 3 is a block diagram showing the configuration of the image capturing device according to the embodiment.

[0035] As shown in FIG. 3, the image capturing device (1000) according to the embodiment includes a first controller (1002), an image capturing module (1200), a first memory (1400), and a first communication module (1800).

[0036] In this embodiment, the first controller (1002) can transmit the medical image acquired by the image acquisition module (1200) to the image analysis device (2000) through the first communication module (1800).

[0037] Hereinafter, each component of the image capturing device 1000 according to the embodiment will be described.

[0038] According to this embodiment, the image acquisition module 1200 can acquire imaging results for an object to be imaged. Here, the image acquisition module 1200 can include a configuration for acquiring various medical images. For example, the image acquisition module 1200 can be configured to acquire a magnetic resonance imaging (MRI) image. For another example, the image acquisition module 1200 can be configured to acquire an X-ray or CT image.

[0039] Here, the first controller 1002 can adjust the setting parameters of the image acquisition module. For example, if the image acquisition module 1200 is configured to acquire MRI images, the first controller 1002 can adjust the repetition time (TR) and echo time (TE) of the MRI imaging device. This allows the MRI imaging device to acquire T1-weighted images or T2-weighted images. The first controller 1002 can also adjust parameters related to an inversion pulse so that the MRI imaging device acquires FLAIR images.

[0040] The first communication module 1800 according to the embodiment can communicate with an external device or an external server. The image capturing device 1000 can perform data communication with the image analyzing device 2000 or an external device (or server) through the first communication module 1800. For example, the image capturing device (1000) can use the first communication module (1800) to transmit medical images or data related to the medical images to the image analyzing device (2000) or an external device.

[0041] The first communication module (1800) is mainly divided into a wired type and a wireless type. Since the wired type and the wireless type each have advantages and disadvantages, in some cases, the image capturing device (1000) may be provided with both a wired type and a wireless type.

[0042] Here, in the case of a wired type, LAN (Local Area Network) or USB (Universal Serial Bus) communication is typical, but other methods may also be used.

[0043] In addition, in the case of a wireless type, a communication method of the WPAN (Wireless Personal Area Network) series such as Bluetooth or Zigbee can be mainly used. However, since the wireless communication protocol is not limited to this, a wireless type communication module can also use a communication method of the WLAN (Wireless Local Area Network) series such as Wi-Fi or other known communication methods.

[0044] The first memory (1400) can store various information. Various data is stored temporarily or semi-permanently in the first memory (1400). Examples of the first memory (1400) include a hard disk drive (HDD), a solid state drive (SSD), flash memory, read-only memory (ROM), and random access memory (RAM).

[0045] The first memory (1400) stores various data necessary for the operation of the image capture device (1000), including an operating program (OS: Operating System) for driving the image capture device (1000) and programs for operating each component of the image capture device (1000).

[0046] The first controller 1002 according to the embodiment may control the overall operation of the image capturing device 1000. For example, the first controller 1002 may receive medical images from the capturing module 1200 and generate a control signal to transmit the images to the data analyzing device 2000 via the first communication module 1800.

[0047] The first controller (1002) can be realized as a CPU (Central Processing Unit) or similar device using hardware, software, or a combination of these. In terms of hardware, it can be provided in the form of an electronic circuit that processes electrical signals and performs control functions, and in terms of software, it can be provided in the form of a program or code that drives a hardware circuit.

[0048] The image capturing device (1000) may have a separate power supply unit, or may be supplied with power from an external source via wire or wirelessly, and may have a switch for controlling the power supply unit.

[0049] FIG. 4 is a block diagram showing the configuration of a video analysis device according to an embodiment.

[0050] Referring to FIG. 4, the image analysis device (2000) includes a second controller (2002), a second memory (2400), a display module (2600), and a second communication module (2800).

[0051] According to this embodiment, the second controller (2002) acquires medical images from the image acquisition device (1000) through the second communication module (2800), analyzes the medical images using an analysis program stored in the second memory (2400), and calculates auxiliary diagnostic information from the medical images.

[0052] Hereinafter, each component of the video analysis device 2000 according to the embodiment will be described.

[0053] The second memory (2400) can store various information of the image analysis device (2000).

[0054] The second memory 2200 can store various data necessary for the operation of the image analysis device 2000, including an operation program for driving the image analysis device 2000 and a program for operating each component of the image analysis device 2000. For example, the second memory 2400 can store a program for processing medical images and / or a program for analyzing the processed medical images. The programs can be implemented as machine-running algorithms, and a detailed description will be given later.

[0055] Examples of the second memory (2400) include a hard disk drive (HDD), a solid state drive (SSD), a flash memory, a read-only memory (ROM), and a random access memory (RAM).

[0056] The second memory (2400) can store various data necessary for the operation of the video analysis device (2000), including an operating program (OS: Operating System) for driving the video analysis device (2000) and programs for operating each component of the video analysis device (2000).

[0057] The second communication module 2800 can communicate with an external device or an external server. The video analysis device 2000 can perform data communication with the video acquisition device 1000 or an external server using the second communication module 2800. For example, the video analysis device 2000 can acquire medical images necessary to provide auxiliary diagnostic information from the video acquisition device 1000 using the second communication module 2800.

[0058] The second communication module (2800) can be broadly divided into wired and wireless types. Since the wired and wireless types each have their own advantages and disadvantages, in some cases, both wired and wireless types can be provided in the video analysis device (2000).

[0059] Here, in the case of a wired type, LAN (Local Area Network) and USB (Universal Serial Bus) communication are typical examples, but other methods are also possible.

[0060] In addition, in the case of a wireless type, a communication method of the WPAN (Wireless Personal Area Network) series such as Bluetooth (registered trademark) or Zigbee can be mainly used. Of course, the wireless communication protocol is not limited to this, and the wireless type communication module can also use a communication method of the WLAN (Wireless Local Area Network) series such as Wi-Fi or other known communication methods.

[0061] The second controller 2002 can control the overall operation of the image analyzer 2000. For example, the second controller 2002 can load a program for processing and analyzing image data from the second memory 2400, process and analyze medical images acquired from the image acquisition device 1000, and generate a control signal to provide the results to an external device or an external server via the second communication module 2800. A specific method for providing auxiliary diagnostic information executed by the image analyzer 2000 will be described in detail later.

[0062] The second controller (2002) can be realized as a CPU (Central Processing Unit) or similar device using hardware, software, or a combination of these. In terms of hardware, it can be provided in the form of an electronic circuit that processes electrical signals and executes control functions, and in terms of software, it can be provided in the form of a program or code that drives a hardware circuit.

[0063] The image analysis device 2000 may further include a separate display module 2600 for outputting the medical image analysis results. The display module may be embodied in various ways to provide information to the user. Here, the second controller 2002 may process and analyze the medical images acquired from the image acquisition device 1000 and generate a control signal for providing the results through the display module 2600.

[0064] Hereinafter, a method for analyzing a medical image and providing auxiliary diagnostic information, which is performed by the auxiliary diagnostic information providing system according to the embodiment, will be described in detail.

[0065] FIG. 5 is a general flowchart of a method for providing auxiliary diagnostic information executed by the auxiliary diagnostic information providing system according to the embodiment.

[0066] Referring to FIG. 5, the operation of providing diagnostic auxiliary information according to the embodiment includes a step of acquiring a medical image (S1000), a step of analyzing the acquired medical image (S1200), and a step of providing diagnostic auxiliary information based on the analysis results (S1400).

[0067] First, the diagnostic auxiliary information providing system (10000) according to the embodiment can acquire medical images from an object to be imaged. Specifically, the image acquisition device (1000) can acquire medical images for acquiring diagnostic auxiliary information from an object to be imaged, such as a patient or a specimen. More specifically, the image acquisition module (1200) in the image acquisition device (1000) can acquire medical images by photographing the object to be imaged, and transmit the acquired medical images to the image analysis device (2000) via the first communication module (1800). Here, there can be multiple medical images. That is, the image acquisition device (1000) can acquire multiple tomographic images from the object to be imaged in a specific direction or on a specific plane.

[0068] Here, the medical images may contain three-dimensional information, i.e., the medical images may be acquired as 3D images.

[0069] In addition, the specific direction and specific plane may include information regarding all directions and planes that can be obtained from the object to be imaged, such as a transverse plane, a sagittal plane, and a coronal plane of the object to be imaged.

[0070] The diagnostic assistance information providing system 10000 can analyze the medical image (S1200). The image analyzing device 2000 can analyze the medical image transmitted from the image capturing device 1000. Specifically, the second controller 2002 can analyze the medical image received through the second communication module 2800 using a program for image analysis stored in the second memory 2400. For example, the second controller 2002 can segment the medical image using the program stored in the second memory 2400.

[0071] Thereafter, the auxiliary diagnostic information providing system (10000) can acquire auxiliary diagnostic information based on the analysis results of the medical image (S1400). That is, the image analyzing device (2000) can acquire auxiliary diagnostic information contained in the analyzed medical image from the medical image using a program for acquiring auxiliary diagnostic information stored in the second memory (2400). The second controller (2002) can transmit the calculated auxiliary diagnostic information to an external device or external server through the second communication module (2800), or can output the auxiliary diagnostic information using a separately prepared display module (2600).

[0072] As described above, the embodiment has generally described the method for providing auxiliary diagnostic information executed by the auxiliary diagnostic information providing system (10000). Below, detailed operations and contents will be described for each step of the method for providing auxiliary diagnostic information executed by the auxiliary diagnostic information providing system (10000).

[0073] First, regarding the medical image acquisition step (S1000), various medical images that can be acquired by the image acquisition device (1000) according to the embodiment will be illustrated and described.

[0074] FIG. 6 shows an example of a medical image according to an embodiment.

[0075] 6, medical images according to embodiments can be acquired by various types of image acquisition devices 1000. The medical images are images acquired by various types of computed tomography devices. As described above, the medical images can also be a collection of tomographic images composed of a plurality of slice images.

[0076] As an example, the medical image is an MRI image taken with an MRI device. (a) and (b) are diagrams showing various body parts of a patient taken with an MRI device. As another example, the medical image is an X-ray image taken with an X-ray device. (c) is a diagram showing an X-ray image of a patient taken with an X-ray device. As another example, the medical image is a CT image taken with a CT device. (d) is a diagram showing a CT image of a patient taken with a CT device.

[0077] 6, the medical image according to the embodiment is an image of each body part, including not only the patient's organs such as the brain and lungs, but also the skeletal structure such as the spine and nervous system.

[0078] According to the embodiment, the information contained in the medical image may vary depending on the setting parameters of the image capture device 1000 that captures the medical image.

[0079] FIG. 7 shows examples of medical images obtained under various acquisition conditions in this embodiment.

[0080] 7, the image acquisition device 1000 according to the embodiment can acquire various images by setting parameters of the device. For example, if the image acquisition device 1000 is implemented as an MRI device, the image acquisition device 1000 can acquire medical images containing various information by adjusting parameters related to magnetic conditions.

[0081] As a more specific example, the image acquisition device 1000 may acquire a T1-weighted image by setting parameters to have a short TR / TE time. The T1-weighted image has a high signal intensity, which helps clearly distinguish anatomical structures. In other words, the T1-weighted image is primarily used to determine the anatomical characteristics of the human body.

[0082] In addition, the image acquisition device (1000) can acquire T2-weighted images by setting parameters to have a long TR / TE time, and can also acquire FLAIR (Fluid Attenuated Inversion Recovery) images by using an inversion pulse. T2-weighted images and FLAIR images are characterized by the fact that areas containing water appear white, and since lesion areas mainly have a high water content, they are effectively used to detect lesion areas.

[0083] In addition, medical images using special imaging techniques such as diffusion weighted imaging (DWI), PET, and fMRI are also included in the concept of the present invention.

[0084] As described above, various examples of medical images acquired in the medical image acquisition step (S1000) according to the embodiment have been described. Hereinafter, the details of the step of analyzing the acquired medical images (S1200) will be described.

[0085] FIG. 8 is a flowchart illustrating a detailed operation of a medical image analysis operation performed by the image analysis device according to the embodiment.

[0086] As shown in FIG. 8, the medical image analysis operation according to the embodiment may include a step of segmenting a medical image (S2002), a step of extracting a disease index from the segmented medical image (S2004), and a step of obtaining diagnostic auxiliary information based on the extracted disease index (S2006).

[0087] First, the image analysis device (2000) can segment a medical image (S2002). Specifically, the second controller (2002) can segment the medical image using an image segmentation program stored in the second memory (2400). Exemplarily, the second controller (2002) can segment a brain-related medical image into brain regions. Here, a brain region may refer to a lesion found in the medical image or a structural region of the brain found in the medical image. That is, a brain region found in the medical image may include all objects that can be distinguished in the image. That is, the second controller (2002) can segment a brain-related medical image into at least two or more regions.

[0088] The program for image segmentation stored in the second memory (2002) can be implemented as a machine-running algorithm, which will be described in detail later.

[0089] After the medical image segmentation step (S2002), the image analyzer (2000) can extract a disease index from the segmented medical image (S2004). Specifically, the second controller (2002) can extract a disease index based on each region included in the segmented medical image using an algorithm stored in the second memory (2400).

[0090] In this specification, a disease index may be defined as an index that expresses the relationship between brain regions in a medical image in relation to a specific disease. Examples of the disease index include the Fazekas scale, ARWMC (Age-related white matter change), Posterior atrophy score of parietal atrophy, MTA (Medial temporal lobe atrophy score), Orbito-Frontal, Anterior Cingulate, Fronto-Insula, Anterior Temporal Scale, etc., and various indexes that can extract disease-related information from medical images may be used.

[0091] After the disease index extraction step (S2004), the image analysis device (2000) can provide auxiliary diagnostic information based on the extracted disease index. Specifically, the second controller (2002) can obtain disease-related information from the medical image based on the extracted disease index using an algorithm stored in the second memory (2400) and output the obtained results.

[0092] Below, an example of a segmented medical image for disease index extraction will be described, and then various embodiments of extracting a disease index from the segmented medical image will be described with reference to FIG.

[0093] FIG. 9 illustrates an example of a segmented medical image according to an embodiment.

[0094] Referring to FIG. 9, an example of a medical image segmented by the video analyzer (2000) is shown.

[0095] In Figure 9, the image analysis device 2000 can segment a medical image. Here, segmentation refers to the image analysis device 2000 assigning specific values ​​to unit cells included in the medical image. Specifically, segmentation refers to the second controller 2002 labeling pixels or voxels included in the medical image with feature values. For example, the second controller 2002 can label multiple pixels included in the medical image with feature values ​​indicating brain regions.

[0096] The image analysis device 2000 can segment medical images to correspond to their form. Here, the form of the medical image can correspond to the acquisition conditions at the time of acquisition of the medical image. For example, if the medical image is captured under specific magnetic conditions, the medical image can have a T1-weighted image form. It should be understood that for the sake of convenience, the terms "medical image form" and "medical image acquisition conditions" are used interchangeably below, but the concept of this specification does not change depending on the use of these terms. In addition, because the characteristics contained in a medical image (e.g., anatomical characteristics or pathological characteristics) may change depending on the form of the medical image or the acquisition conditions of the medical image, the term "medical image characteristics" should also be used interchangeably.

[0097] Specifically, the second controller (2002) can segment the medical image using a program for image segmentation stored in the second memory (2400) so that the characteristics of the medical image can be reflected.

[0098] The second controller (2002) can label pixels included in the medical image with values ​​indicating brain regions. That is, in a medical image embodied as a FLAIR image, the second controller (2002) can label pixels distinguished by white matter, gray matter, ventricles, etc. with values ​​indicating the respective regions.

[0099] Illustratively, (a) shows the image analyzer (2000) segmenting a T1-weighted MRI image, and (b) shows the image analyzer (2000) segmenting a T2-FLAIR MRI image.

[0100] As shown in the drawing, the T1-weighted MRI image (a) facilitates understanding of the anatomical characteristics of the brain. That is, when the image analysis device (2000) segments the T1-weighted MRI image, the image analysis device (2000) can perform segmentation to distinguish each part of the brain. For example, the image analysis device (2000) according to the embodiment can segment the brain image to distinguish organs contained in the brain, such as the cerebrum, cerebellum, diencephalon, and hippocampus. It can also segment the brain to distinguish each part of the brain, such as the temporal lobe, frontal lobe, and occipital lobe, or a combination of these. In this way, when analyzing the T1-weighted MRI image, which clearly shows the anatomical characteristics of the brain, to obtain auxiliary diagnostic information, the image analysis device (2000) can easily analyze disease information related to the anatomical characteristics of the brain, as well as atrophic Alzheimer's disease.

[0101] Furthermore, the T2 FLAIR MRI image (b) facilitates understanding of brain pathological characteristics. When segmenting a T2-FLAIR MRI image using the image analysis device (2000) according to the embodiment, the image analysis device (2000) can segment the medical image so as to distinguish brain pathological characteristics. Here, brain pathological characteristics can be detected by white matter hyperintensity (hereinafter referred to as "WMH") observable in the medical image. That is, the image analysis device (2000) can segment the medical image so as to distinguish WMH from materials constituting the brain and other regions in the T2-FLAIR MRI image. Here, materials constituting the brain (or the human head) include white matter, gray matter, skull, etc., and other regions include ventricles, etc. The image analyzer (2000) can extract disease indices based on the location of WMHs and their relationship to other brain structures, which will be explained in more detail later.

[0102] Although the drawings mainly illustrate T1-weighted MRI images and T2-FLAIR MRI images as examples, as mentioned above, the concept of the present specification is not limited thereto, and MRI images taken under other magnetic conditions such as DWI and SWI, and medical images taken with other imaging devices such as X-ray and CT can all be used.

[0103] The image analyzer (2000) can also perform segmentation so that a medical image having a specific characteristic (i.e., acquired under specific acquisition conditions) includes information about other characteristics. Specifically, the second controller (2002) can perform segmentation using a machine-running algorithm stored in the second memory (2400) so that a medical image acquired under a first condition and having a first characteristic includes not only the first characteristic but also information about a second characteristic that can be acquired when acquired under a second condition. For example, when segmenting a T2-FLAIR MRI image, the image analyzer (2000) can segment the T2-FLAIR image so that anatomical information observable in a T1-weighted MRI image is also included. This will be described in detail below with reference to the drawings.

[0104] Hereinafter, one embodiment of a specific method for extracting a disease index from a segmented medical image will be described with reference to the accompanying drawings.

[0105] Fig. 10 is a flowchart showing an example of a method for extracting a disease index from a medical image in this embodiment, Fig. 11 shows an example of a medical image in which a reference region and a reference boundary are illustrated, Fig. 12 shows an example of acquiring region information of a target region, Fig. 13 shows another example of acquiring region information of a target region, and Fig. 14 shows an example of a case in which auxiliary diagnostic information acquired based on a calculated disease index is provided.

[0106] First, a general flow will be described with reference to Figure 10. In this embodiment, the method for extracting a disease index from a medical image segmented by the video analysis device (2000) includes the steps of detecting a reference region from the segmented medical image (S2102), setting a reference boundary based on the detected reference region (S2104), and extracting a disease index based on the relationship between the reference boundary and the target region (S2106).

[0107] The image analysis device (2000) can detect a reference region from the segmented medical image (S2102). Specifically, the second controller (2002) can detect labeled pixels in the segmented medical image to indicate the reference region and determine a set of these pixels as the reference region. Here, the reference region refers to a region included in the medical image that serves as a reference for calculating a specific disease index, as described below. As an example, the reference region is a ventricle region. As another example, the reference region is a white matter region.

[0108] Here, the reference area is an area in which a buffer area of ​​a certain size or more is added to a set of pixels labeled to indicate the reference area. The reference area may be defined as an area in which certain pixels on the outer periphery of the set of pixels labeled to indicate the reference area are removed.

[0109] The image analysis device (2000) can also detect a region corresponding to the analysis target from the segmented medical image. Specifically, the second controller (2002) can detect pixels labeled to indicate the analysis target and determine a set of these pixels as the analysis target region (hereinafter referred to as the "target region"). Here, the target region may be included in the medical image as described below, and may also refer to a region that is the target of analysis for calculating a specific disease index. As an example, the target region may refer to the WMH region. The method for detecting the target region is similar to the method for detecting the ventricle region described above, and therefore a detailed description thereof will be omitted.

[0110] When the reference region is detected, the image analysis device (2000) can set a reference boundary based on the detected reference region (S2104). Specifically, the second controller (2002) can set a boundary that is a predetermined distance away from the ventricle region detected in the medical image as the reference boundary.

[0111] Here, the reference boundary refers to a boundary that serves as a reference for deriving the relationship between the reference region and the target region and extracting a disease index, as described below. That is, when a disease index such as the Fazekas scale is used to determine disease information related to Alzheimer's, the positional relationship between the ventricle and the WMH can be used as an important index, and the reference boundary is used as a reference for understanding the positional relationship between the ventricle and the WMH. In this case, the ventricle can be determined as the reference region of the Fazekas scale, and the WMH can be determined as the target region of the Fazekas scale.

[0112] To obtain the reference boundary, a predetermined distance can be calculated from any point within the reference area. For example, the reference boundary may refer to a set of pixels that are a predetermined distance away from pixels on the outer edge of the reference area. In this case, the reference direction for the predetermined distance is preferably the normal direction of the boundary surface of the reference area. Alternatively, the reference boundary may refer to a set of pixels that are a predetermined distance away from the center of the reference area.

[0113] The reference boundary will be described with reference to FIG.

[0114] Figure 11 shows the segmentation results of the ventricles, WMH, white matter, gray matter, skull, etc. in a T2-FLAIR MRI image.

[0115] The image analysis device 2000 can use the Fazekas scale as a disease index. Specifically, the second controller 2002 can calculate the Fazekas scale by determining the ventricular region as a reference region and the WMH region as a target region. Here, the second controller 2002 can set a set of pixels a predetermined distance away from pixels labeled as a ventricle as the reference boundary for calculating the Fazekas scale. The reference boundary can also be calculated from pixels on the periphery of the ventricular region, and the predetermined distance can be 10 mm. However, this is merely an example value, and any distance within a range of 5 mm to 15 mm can be determined.

[0116] Referring to FIG. 11, WMHs are shown to exist both inside and outside the reference boundary. The WMHs existing inside and outside the reference boundary act as important factors in determining auxiliary diagnostic information. Specifically, the relationship between information about the area occupied by the WMHs (hereinafter referred to as "area information") and the reference boundary is closely related to the auxiliary diagnostic information. A method for calculating area information of WMHs from a medical image in which WMHs have been segmented will be described with reference to FIGS. 12 and 13.

[0117] Medical research has shown that, in general, white matter regions relatively close to the ventricles tend to degenerate more easily than white matter regions relatively far from the ventricles. Therefore, WMH regions formed around the ventricles in medical images and WMH regions relatively far from the ventricles can each contain different information about disease. Therefore, when analyzing WMH regions in medical images, it is necessary to take their distance from the ventricles into consideration.

[0118] 12 and 13 show examples of methods for obtaining region information related to a target region, taking into account the distance between the reference region and the target region.

[0119] As shown in Figure 12, the image analysis device 2000 according to the embodiment can acquire region information of a target region in a medical image. Specifically, the second controller 2002 can acquire region information for the target region in the medical image based on the segmentation result. Here, the region information refers to the thickness or width of a specific region. Furthermore, as will be described later, when the medical image is embodied in 3D, the region information refers to the volume.

[0120] First, when calculating a disease index based on the Fazekas scale, a method for calculating region information of a target region that exists near a reference region included in a medical image, i.e., that exists within the reference boundary, will be described.

[0121] The image analysis device 2000 can acquire region information of the target region based on the distance from the reference region to the target region. Specifically, the second controller 2002 can calculate region information of the target region based on the distance from the region labeled as the reference region to the region labeled as the target region in the medical image.

[0122] For example, the second controller (2002) can calculate the thickness of the WMH region based on the distance from the region labeled as a ventricle in the medical image to the region labeled as a WMH. Here, the distance can be defined in the normal direction from the boundary of the region labeled as a ventricle. Furthermore, the largest length among the calculated WMH thicknesses can be measured as the WMH thickness. More specifically, the second controller (2002) can determine the WMH region located closest to the boundary of the region labeled as a ventricle in the normal direction as a first point and the WMH region located farthest from the boundary as a second point, and determine the distance between the first point and the second point as the thickness (width) of the WMH region. Here, if the WMH region is connected (adjacent) to the ventricle region, the first point can be determined to be a point on the ventricle.

[0123] In other words, the second controller (2002) can generate a ray from the boundary of the ventricular region in the medical image in the normal direction and calculate the thickness of the WMH by considering the junction between the generated ray and a pixel labeled as a WMH. Here, the junction between the ray and the pixel labeled as a WMH can include a first junction that measures the closest distance between the ventricular region and the WMH and a second junction that measures the furthest distance. That is, the second controller (2002) can calculate the thickness (width) of the WMH by considering the distance between the first junction and the second junction. Here, it is as described above that the first junction can exist on the ventricular region.

[0124] The method using rays is preferably applied to WMHs located within the reference boundary described above, but is not limited thereto, and the same method can be applied to WMHs outside the reference boundary.

[0125] FIG. 13 also illustrates a method for calculating area information for WMHs other than the reference boundary.

[0126] 13, the image analysis device 2000 can obtain region information of the target region using principal component analysis. Specifically, the second controller 2002 can extract at least two principal components of the target region using principal component analysis and calculate region information of the target region based on the extracted principal components.

[0127] For example, the second controller (2002) can extract principal components along at least two axes in the region labeled as a WMH in the medical image. While the principal components can be extracted in various ways, it is preferable to extract them along the short axis and long axis in the region labeled as a WMH. Once the principal components are extracted, the second controller (2002) can calculate the thickness of the region labeled as a WMH based on the extracted principal components.

[0128] In other words, the second controller (2002) can perform principal component analysis on the region labeled as a WMH to obtain the direction and size of the major and minor axes, and calculate the thickness of the region labeled as a WMH based on the length of the major axis. Alternatively, the second controller (2002) can calculate the width of the WMH region.

[0129] The above describes a method for calculating region information of a target region from a medical image, which is used to obtain a disease index. It also explains that the positional relationship between the target region and a reference region is taken into consideration when obtaining region information of the target region.

[0130] Hereinafter, referring again to FIG. 10, a method for extracting a disease index based on the calculated WMH area information and reference boundary will be described.

[0131] Referring again to FIG. 10, the image analyzer (2000) can calculate a disease index based on the reference boundary set in the medical image and the region information of the target region (S2106).

[0132] Specifically, the second controller (2002) can calculate the disease index by comprehensively considering the association relationship between the reference boundary and the target area, and the area information of the target area.

[0133] Here, an example of the association relationship is whether or not a WMH is located inside or outside the reference boundary.

[0134] As a specific example, it is generally believed that the thicker the WMH, the more serious the disease. However, the method for extracting a disease index according to an embodiment can comprehensively consider not only the thickness of the WMH but also whether the WMH is located within or outside the reference boundary.

[0135] JPEG0007778409000001.jpg48170

[0136] JPEG0007778409000002.jpg77170

[0137] Table 1 shows an example of the criteria for calculating the disease index based on the subjective opinion of existing doctors.

[0138] Table 1 shows an example of a standard for calculating the Fazekas scale currently used in the medical industry. The Fazekas scale is a method for calculating disease indices that has been published in various papers and is used in the medical industry as a credible method for calculating disease indices. However, as shown in Table 1, the existing medical industry calculates disease indices based on vague criteria and subjective judgments of doctors, which has led to the problem of being unable to provide patients with objective diagnostic information or diagnostic support information.

[0139] Table 2 shows the criteria for calculating the disease index performed by the image analysis device of this embodiment.

[0140] In contrast, referring to Table 2, the image analysis device (2000) according to the embodiment can calculate the disease index according to a specific standard. Specifically, the second controller (2002) can calculate the disease index from the medical image according to the standard for calculating the disease index stored in the second memory (2400).

[0141] Here, the criteria for the image analysis device (2000) to calculate the disease index can be determined based on various diagnostic or diagnostic assistance results previously obtained in the medical industry. That is, according to this embodiment, the image analysis device (2000) can calculate the disease index from the medical image using the criteria quantified as a result of analyzing a plurality of medical images previously obtained in the medical industry and for which the disease index has been calculated.

[0142] That is, the image analyzer (2000) can analyze the medical image, determine a disease index to be calculated according to a predetermined standard, and calculate a grade of the disease index corresponding to the determined disease index. Specifically, the second controller (2002) can determine a reference boundary from the reference area, determine a disease index to be calculated in consideration of the relationship between the determined reference boundary and the target area, and calculate a grade value corresponding to the determined disease index.

[0143] Here, the predetermined criterion for determining the disease index to be calculated may be the reference boundary described above. For example, the type of disease index calculated by the image analysis device 2000 may change depending on whether the target area is within the reference boundary or whether the target area is outside the reference boundary.

[0144] Here, in calculating the same disease index, there may be a criterion for distinguishing the grade of the disease index. In one example, the predetermined criterion is a criterion related to the area information of the target area.

[0145] Referring again to Table 2, the disease index according to the embodiment may include first to third disease indexes. The disease indexes shown in Table 2 represent the Fazekas scale. The first disease index is a disease index related to periventricular white matter, the second disease index represents a disease index related to deep white matter, and the third disease index may represent a total index calculated taking into account the first and second disease indexes.

[0146] Here, the reference boundary can be used to distinguish between the first disease index and the second disease index. That is, as described above, the second controller (2002) can use the reference boundary to calculate the first disease index from the target area within the reference boundary. The second controller (2002) can use the reference boundary to calculate the second disease index from the target area outside the reference boundary.

[0147] In addition, the second controller (2002) can distinguish between grades of the same disease index, that is, the second controller (2002) can calculate a grade value corresponding to a specific disease index from a medical image according to a predetermined criterion.

[0148] Here, the predetermined criterion may relate to area information of the target area. In one example, if there is no cell indicating the target area or if it is determined that the number of cells is less than a predetermined number, the second controller (2002) may determine that the grade value of the specific disease index is 0. In another example, if the thickness of the target area is measured to be equal to or greater than a first value, the second controller (2002) may determine that the grade value of the specific disease index is 1. In another example, if the thickness of the target area is measured to be equal to or greater than a second value, the second controller (2002) may determine that the grade value of the specific disease index is 2. In yet another example, if the thickness of the target area is measured to be equal to or greater than a third value or if it is connected to another target area, the second controller (2002) may determine that the grade value of the specific disease index is 3.

[0149] That is, in the example shown in Table 2, the second controller (2002) can calculate a first disease index based on the target area included within the reference boundary as a result of analyzing the medical image, and can calculate a grade value associated with the first disease index taking into account the area information of the target area included within the reference boundary.

[0150] In addition, the image analyzer (2000) can calculate all values ​​related to the first disease index and the second disease index within the same medical image. If the segmentation result of the medical image shows that all target regions exist both inside and outside the reference boundary, the second controller (2002) can calculate all grades related to the first disease index and the second disease index.

[0151] The image analysis device (2000) can also calculate a third disease index by taking into account the first disease index and / or the second disease index.

[0152] FIG. 14 shows an example of a display that provides disease indexes and diagnostic support information based on the disease indexes in an embodiment.

[0153] 14, the image analysis device 2000 can provide auxiliary diagnostic information based on a disease index. Specifically, the second controller 2002 can obtain a disease index based on the analysis result of the medical image, and can provide auxiliary diagnostic information based on the obtained disease index.

[0154] To explain a specific example with reference to the drawings, the second controller (2002) can provide a disease index for the target area within the reference boundary based on the analysis result of the medical image, and can also provide information on the meaning of the disease index based on the disease index.

[0155] As described above, the disease index refers to a scale (or standard) for the progression or severity of a disease that can be determined within a medical image. For example, if the standard for the disease index is the Fazekas scale, the disease index can be expressed as a grade on the Fazekas scale.

[0156] In addition, the auxiliary diagnostic information refers to information related to a disease that can be determined based on the disease index. For example, if the disease index is calculated as a specific grade using the Fazekas scale, the auxiliary diagnostic information refers to disease-related information that can be inferred from the disease index, such as the current progression of the disease, the difference from the normal range, the difference from the average for each age, etc.

[0157] The diagnostic auxiliary information that can be obtained from the medical image and the diagnostic auxiliary information to be compared, such as average values ​​by age, disease index values ​​of normal people, etc., can be obtained in advance and stored in the second memory (2400), and can be continuously updated.

[0158] As explained above, the image analysis device (2000) sets clear criteria, extracts disease indexes from medical images, and provides diagnostic auxiliary information based on the extracted disease indexes. This has the effect of making it possible to obtain more objective and accurate diagnostic auxiliary information than disease-related information that has previously relied on the subjective judgment of doctors in the medical industry.

[0159] As described above, a method using a predetermined reference boundary has been described as an example of a method for providing auxiliary diagnostic information. However, rather than using a fixed reference boundary according to the characteristics of a medical image, it may be necessary to modify the reference boundary in consideration of the characteristics of the medical image in order to provide more accurate auxiliary diagnostic information.

[0160] Hereinafter, a method using a modified reference boundary will be described as another example of a method for providing diagnostic auxiliary information with reference to the drawings.

[0161] First, with reference to FIG. 15, another embodiment for extracting disease indexes from medical images to obtain disease information will be generally described.

[0162] FIG. 15 is a flowchart illustrating an example of a method for utilizing modified reference boundaries to provide diagnostic assistance information, according to an embodiment.

[0163] Referring to FIG. 15, a method for providing diagnostic auxiliary information according to an embodiment includes a step of detecting a reference area from a segmented medical image (S2202), a step of setting a reference boundary based on the detected reference area (S2204), a step of determining whether the reference boundary overlaps with a region of non-interest (S2206), a step of modifying the reference boundary (S2208), and a step of obtaining a disease index based on the modified reference boundary (S2210).

[0164] The video analysis device (2000) performs operations similar to those performed in the embodiment described with reference to Figures 10 to 14, namely, a reference area detection step (S2202) and a reference boundary setting step (S2204) based on the detected reference area, and therefore detailed explanations will be omitted here.

[0165] Once the reference boundary is set, the image analysis device 2000 can determine whether the set reference boundary overlaps with a region of non-interest in the segmented medical image (S2206). Specifically, the second controller 2002 can determine whether pixels corresponding to the set reference boundary in the medical image are included in pixels labeled with a value indicating the region of non-interest. Here, the region of non-interest is a gray matter region.

[0166] Here, a description will be given with reference to FIG.

[0167] FIG. 16 illustrates an example where the reference boundary and the region of no interest are overlapped according to an embodiment.

[0168] Referring to FIG. 16, a fiducial boundary is shown overlapping a gray matter labeled region in a medical image.

[0169] Generally, WMHs occur primarily in white matter, but are not found in gray matter. For this reason, if a disease index is extracted as it is in the past when the reference boundary invades the gray matter region, the disease index may be calculated inaccurately, and as a result, the diagnostic support information based on the disease index is likely to be inaccurate as well. Therefore, if the reference boundary overlaps with the gray matter region, the reference boundary must be modified and the disease index must be calculated using the modified reference boundary.

[0170] In the remainder of this specification, we will refer to regions where target regions (e.g., WMHs) can be observed, such as white matter regions, as "regions of interest," and to regions where target regions (e.g., WMHs) cannot be observed, such as gray matter regions, as "regions of non-interest."

[0171] Therefore, the second controller (2002) can segment the medical image to set a reference boundary, and then modify the reference boundary if the reference boundary overlaps with at least a portion of the area labeled with gray matter (S2208).

[0172] 15 again, if the reference boundary is modified, the image analysis device 2000 can obtain a disease index based on the modified reference boundary (S2210). The method of obtaining the disease index is similar to the embodiment described with reference to FIGS. 10 to 14, so a detailed description will be omitted.

[0173] An example of correcting the reference boundary will be described below with reference to the drawings.

[0174] 17a and 17b show examples of how to calculate the modified reference boundary in this embodiment.

[0175] 17a and 17b, the image analysis device 2000 may modify the reference boundary. Specifically, the second controller 2002 may modify the reference boundary so that the modified reference boundary is positioned between an area labeled as a non-region of interest and an area labeled as a reference region. Here, the reference boundary may be positioned so that the distance between the reference region and the non-region of interest is divided internally at a predetermined ratio. Here, the predetermined ratio may be set in consideration of various factors. For example, the predetermined ratio may be determined in consideration of the distance between the non-region of interest and the reference region. That is, the second controller 2002 may modify the reference boundary in consideration of various factors that can be considered in the medical image, including the distance between the reference region and the non-region of interest, the position of the reference boundary overlapping with the non-region of interest, and / or the shape of the reference region.

[0176] 17A, the distance between the region of non-interest (gray matter) and the reference region (ventricles) is relatively close, and the reference boundary overlaps a relatively large portion of the region of non-interest. In this case, the second controller can correct the reference boundary by taking into account the distance between the region of non-interest and the reference region, so that the corrected reference boundary is located midway between the gray matter region and the ventricle region (at the point where the distance between the ventricles and the gray matter is divided 5:5).

[0177] 17b, the distance between the region of non-interest (gray matter) and the reference region (ventricles) is relatively far, and the reference boundary overlaps a relatively small portion of the region of non-interest. In this case, the second controller (2002) can modify the reference boundary so that the modified reference boundary is closer to the region of non-interest.

[0178] In addition, according to this embodiment, the reference boundary determined in one medical image may overlap with multiple regions of non-interest, i.e., the situations shown in Figures 17(a) and 17(b) may occur within one medical image.

[0179] In such a case, the second controller (2002) can modify the reference boundary so that the first and second sections overlapping with the region of non-interest within the reference boundary have different ratios. Illustratively, the second controller (2002) can modify the reference boundary in the first section as shown in Figure 17a, and in the second section as shown in Figure 17b.

[0180] Expressed differently, the second controller (2002) can set the ratio of the distance from the reference area to the reference boundary to the distance from the reference boundary to the region of non-interest to a first value when the distance between the region of non-interest and the reference area is a first distance, and can correct the reference boundary so that the ratio of the distance from the reference area to the distance from the reference boundary to the region of non-interest to a second value when the distance between the region of non-interest and the reference area is a second distance. Here, when the second distance is greater than the first distance, the reference boundary can be corrected so that the second value is smaller than the first value. For example, the greater the relative distance between the reference area and the region of non-interest, the more the corrected reference boundary can be positioned closer to the region of non-interest.

[0181] As a specific example, in the case of a first region where the distance between the gray matter and the ventricles is relatively short, the second controller (2002) can modify the reference boundary so that the reference boundary divides the distance between the gray matter and the ventricles at a ratio of 5:5, and in the case of a second region where the distance between the gray matter and the ventricles is relatively long, the reference boundary can modify the reference boundary so that the distance between the gray matter and the ventricles is divided at a ratio of 3:7. Here, the ratio for dividing the distance between the gray matter and the ventricles can be preset.

[0182] The second controller (2002) can also modify the reference boundary only for the area where the reference boundary overlaps with the region of non-interest, or can modify the entire reference boundary. Furthermore, the second controller (2002) can also modify the reference boundary for an added buffer area in addition to the area where the reference boundary overlaps with the gray matter area. This will be described in detail with reference to FIG. 18.

[0183] FIG. 18 shows an example of a partially modified reference boundary and a fully modified reference boundary according to an embodiment.

[0184] 18, the image analysis device 2000 can modify all or part of the reference boundary. Specifically, the second controller 2002 can modify all or part of the reference boundary in consideration of the relationship between the reference area and the area of ​​non-interest.

[0185] For example, if the distance between the reference region and the region of non-interest is close only in a specific portion, the reference boundary can be modified only in a portion of the reference boundary corresponding to that specific portion, and if the distance between the region of non-interest and the reference region is similar overall, the reference boundary can be modified overall. However, this is merely an example, and there may be various situations in which the reference boundary is modified overall or in part.

[0186] An example will be described with reference to the drawings, which show a fully modified fiducial boundary and a partially modified fiducial boundary.

[0187] An example in which the reference boundary is generally modified will be described first.

[0188] The image analyzer 2000 can globally correct the reference boundary. Specifically, the second controller 2002 can globally correct the reference boundary in consideration of the distance between the reference region and the region of non-interest.

[0189] The second controller (2002) can modify the entire reference boundary in a section where the reference boundary and the region of non-interest overlap, so as to correspond to the modification of the reference boundary.

[0190] Alternatively, the second controller (2002) can modify the reference boundary taking into account the distance between the reference region and the region of non-interest in all sections of the reference boundary, not just in the section where the reference boundary and the region of non-interest overlap.

[0191] For example, it can be expressed that the second controller (2002) can generally modify the reference boundary to correspond to a position that is an internal division of the distance between the non-interest area and the reference area in the first area, taking into account the distance between the non-interest area and the reference area in the first area.

[0192] The second controller (2002) can also generally correct the reference boundary for all sections of the reference boundary, taking into account the distance between the general area of ​​the reference region and the area of ​​no interest.

[0193] In addition, the image analysis device 2000 can modify only a portion of the reference boundary. Specifically, the second controller 2002 can modify the reference boundary only for a partial section corresponding to an overlapping section of the reference boundary and the region of non-interest. Here, the second controller 2002 can modify the reference boundary also for a portion where a buffer section is added to an overlapping section of the reference boundary and the region of non-interest, or can modify the reference boundary only for a section where a partial section is removed.

[0194] In other words, the second controller (2002) can determine the reference boundary so that a first distance exists between the reference boundary and the reference area for a section where the distance between the reference boundary and the region of non-interest is closer than a predetermined distance, and can determine the reference boundary so that a second distance exists between the reference boundary and the reference area for a section where the distance between the reference boundary and the region of non-interest is farther than the predetermined distance. Here, the predetermined distance can correspond to the distance between the reference boundary and the reference area described with reference to FIGS. 10 to 14.

[0195] Referring to the drawings, the partially corrected reference boundary, the globally corrected reference boundary 1, and the globally corrected reference boundary 2 all have similar shapes in sections where the region of non-interest (gray matter) and the reference region are close to each other. However, the globally corrected reference boundary 1 appears to be separated from the reference region by the same distance even in sections where the distance between the region of non-interest and the reference region is greater. In contrast, the globally corrected reference boundary 2 is determined so that the reference boundary is located at a position that divides the region of non-interest and the reference region at a predetermined ratio in all sections of the reference boundary.

[0196] However, even in the case of a reference boundary that is only partially modified, it is necessary to smoothly connect the modified area of ​​the reference boundary with the existing reference boundary so that the modified reference boundary maintains a soft shape.

[0197] Hereinafter, the connection relationship between the existing reference boundary and the modified reference boundary when a portion of the reference boundary is modified will be described with reference to the drawings.

[0198] FIG. 19 shows an example of a specific modification process for a partially modified reference boundary according to an embodiment.

[0199] 19, the image analysis device 2000 can connect a modified reference boundary to an existing reference boundary. Specifically, the second controller 2002 can determine the range of the reference boundary to be modified by considering the distance between the reference area and the area of ​​non-interest, and can connect a portion of the modified reference boundary to the existing reference boundary.

[0200] First, the second controller (2002) can modify the reference boundary from a first point where the distance between the reference area and the area of ​​non-interest is equal to or less than the critical distance to a second point where the distance between the reference area and the area of ​​non-interest is equal to or less than the critical distance. However, this is merely an example, and the first point can be selected as any point where the reference boundary and the area of ​​non-interest overlap, or any point where the distance between the area of ​​non-interest and the reference area is equal to or less than a predetermined distance. Furthermore, the method of modifying the reference boundary can be any of the methods described in the above embodiments or other methods integrated with the concept of the present invention.

[0201] Thereafter, the second controller 2002 may connect the first or second point, which is the end point of the area where the reference boundary has been modified, to the third point, which is the section where the existing reference boundary begins again. Here, the distance between the third point and the reference area according to the existing reference boundary may be predetermined as described with reference to FIGS. 10 to 14.

[0202] The second controller 2002 may interpolate between the position value of the first or second point and the position value of the third point to connect the first or second point with the third point. Here, the length of the interpolation section between the second and third points may be set in various ways. For example, the interpolation section may be set in consideration of the distance between the region of interest and the region of non-interest.

[0203] A specific example will be described with reference to the drawings.

[0204] The second controller (2002) can partially modify the reference boundary where the distance between the gray matter and the ventricles is short or where the existing reference boundary overlaps with the gray matter region. The second controller (2002) can interpolate between a first point located on one side of the modified reference boundary and a third point located on the existing reference boundary. Here, the second point can be selected as a point where the distance between the gray matter and the ventricles is equal to or greater than a critical distance. Alternatively, the second point can be selected as a point where the distance between the modified reference boundary and a region of no interest is equal to or greater than a critical distance.

[0205] In this way, the image analysis device (2000) according to the embodiment can set a reference boundary for calculating a disease index more clearly by smoothly connecting the existing reference boundary and the modified reference boundary, and the clearly set reference boundary can improve the accuracy of the disease index obtained by the image analysis device (2000).

[0206] If the disease index is calculated using the modified reference boundary, the image analysis device (2000) can provide auxiliary diagnostic information based on the disease index obtained using the modified reference boundary. Specifically, the second controller (2002) can obtain a disease index from the analysis result of the medical image using the modified reference boundary, and can provide auxiliary diagnostic information based on the obtained disease index. The details of the provided disease index or disease information are similar to those described in FIG. 14, so detailed description will be omitted.

[0207] As explained above, by using a modified reference boundary, it is possible to obtain an accurate disease index from a wider variety of images than when a uniform reference boundary is used.

[0208] In addition, although the above explanation has been given on modifying the reference boundary only when it overlaps with the gray matter region, it is also possible to set the reference boundary from the beginning as described in Figures 15 to 19, and an appropriate method for setting the reference boundary can be selected according to the requirements of the field.

[0209] The above description of this specification has primarily focused on a method for analyzing a single medical image.

[0210] The following describes a method for providing auxiliary diagnostic information from a plurality of medical images when the image analysis device (2000) analyzes the plurality of medical images.

[0211] FIG. 20 is a schematic flow chart illustrating how the image analyzer (2000) provides auxiliary diagnostic information from multiple medical images.

[0212] As shown in FIG. 20, the method for providing diagnostic auxiliary information from multiple medical images includes a step of acquiring multiple medical images (S3200), a step of acquiring disease indexes from the multiple medical images (S3400), and a step of providing diagnostic auxiliary information based on the multiple disease indexes (S3600).

[0213] First, the image analysis device 2000 can acquire a plurality of medical images (S3200). Specifically, the second controller 2002 can acquire the plurality of medical images from the image acquisition device 1000 through the second communication module 2800. Here, the plurality of medical images is a collection of tomographic images of the object to be imaged. For example, the plurality of medical images is a collection of MRI slice images of the object to be imaged.

[0214] Hereinafter, the image analysis device (2000) can acquire disease indexes from a plurality of medical images (S3400). Specifically, the second controller (2002) can acquire disease indexes for the plurality of medical images using a program for acquiring disease indexes stored in the second memory (2400). Here, the second controller (2002) can acquire disease indexes for each of the plurality of medical images, or can acquire disease indexes for some of the plurality of medical images. The method for acquiring disease indexes is similar to that described above, so a detailed description will be omitted.

[0215] If multiple disease indexes are acquired, the image analysis device (2000) can provide auxiliary diagnostic information based on the multiple disease indexes (S3600). Specifically, the second controller (2002) can acquire and provide auxiliary diagnostic information by comprehensively considering the disease indexes for the multiple acquired medical images. Here, the second controller (2002) can provide auxiliary diagnostic information for all of the multiple medical images for which disease indexes have been calculated, or can provide auxiliary diagnostic information for only some of the multiple medical images for which disease indexes have been calculated. The method for providing auxiliary diagnostic information for multiple medical images by the image analysis device (2000) will be described in detail below.

[0216] Hereinafter, examples of a plurality of medical images and a method for providing auxiliary diagnostic information based on the plurality of medical images according to an embodiment will be described with reference to the accompanying drawings.

[0217] FIG. 21 shows an example of multiple medical images according to an embodiment.

[0218] 21, the image analysis device 2000 can acquire a plurality of tomographic images of the object to be imaged. Specifically, the second controller 2002 can acquire a plurality of medical images including a plurality of cross-sectional images of the object to be imaged through the second communication module 2800. Here, the plurality of tomographic images are composed of a set of slice images, as described above.

[0219] FIG. 21 illustrates an example of a number of medical images acquired with an MRI imaging device.

[0220] 21, the medical images include slice images of a plurality of planes parallel to a first axis of the object being imaged. Each slice image may include information about the object being imaged at a predetermined distance from each other. That is, each slice image includes different information about the object being imaged. In this way, the medical images collectively include information about various cross sections of the object being imaged, and the image analysis device 2000 can obtain auxiliary diagnostic information about the object being imaged overall by analyzing the medical images.

[0221] FIG. 22 shows an example of slice images each containing other information and an example of the provision of diagnostic auxiliary information therefrom.

[0222] 22 shows an example of a plurality of medical images of a single object taken along different cross sections. Specifically, FIG. 22 shows a collection of various slice images of an MRI image of a human brain.

[0223] (a) shows an MRI slice image of the upper section of the brain, and (b) shows an MRI slice image of the lateral section of the brain.

[0224] Compared to (b), (a) is a cross-sectional image of the upper brain region, and the size of the ventricles is smaller than that of (b). Also, the white matter and gray matter regions in (a) and (b) are depicted differently, and most importantly, they contain different regional information distinguished by WMH.

[0225] As described above, the disease index in this embodiment can be obtained based on the relationship between the region information of the reference region and the target region. Therefore, different disease indexes can be calculated from each of a plurality of medical images including cross sections at various positions of the subject. As a specific example, when a cross section of the upper side of the brain is imaged (a), a disease index of level 3 is calculated, and when a cross section of the inside of the brain is imaged (b), a disease index of level 2 is calculated.

[0226] In such a case, auxiliary diagnostic information regarding (a) and auxiliary diagnostic information regarding (b) are all acquired, and the image analysis device (2000) can acquire and provide auxiliary diagnostic information for all images or auxiliary diagnostic information for some images.

[0227] That is, the image analysis device (2000) according to the embodiment can calculate disease indexes for a plurality of medical images obtained by photographing various cross sections of the subject, and can provide diagnostic auxiliary information based on the calculated disease indexes.

[0228] In addition, the image analyzer (2000) can provide auxiliary diagnostic information for all medical images for which disease indexes have been calculated, or can provide auxiliary diagnostic information for only some of the medical images.

[0229] The image analyzer (2000) can select some images from the medical images for which a disease index has been calculated, and provide auxiliary diagnostic information for the selected medical images. Specifically, the second controller (2002) can select images containing predetermined information from the medical images for which a diagnostic index has been calculated, calculate a disease index for the images containing the predetermined information, and provide auxiliary diagnostic information.

[0230] Here, there are various criteria for selecting medical images for providing auxiliary diagnostic information, that is, the image analyzer 2000 may select medical images for providing auxiliary diagnostic information based on the type or grade of disease index.

[0231] For example, the image analysis device 2000 may provide auxiliary diagnostic information for a medical image that is the basis for calculating a disease index level, which indicates the most severe disease progression level, among the medical images for which a disease index is calculated. As another example, the image analysis device 2000 may provide auxiliary diagnostic information for a medical image that is the basis for calculating a disease index level, which indicates the most improved disease state, among the medical images for which a disease index is calculated. As yet another example, the image analysis device 2000 may provide auxiliary diagnostic information for a medical image that is the basis for calculating a disease index level, which indicates the average (or median) of disease indices calculated from multiple medical images, among the medical images for which a disease index is calculated.

[0232] As another example, the image analysis device 2000 may provide auxiliary diagnostic information for a medical image in which a first disease index is measured to be high, or for a medical image in which a second disease index is measured to be high, taking into account the type of disease index, or may provide auxiliary diagnostic information by combining these. It is also possible for the image analysis device 2000 to select medical images for providing auxiliary diagnostic information, taking into account the type and grade of the disease index.

[0233] In this way, other auxiliary diagnostic information may be calculated depending on the type and grade of the disease index, so the image analysis device (2000) can provide auxiliary diagnostic information suited to the patient by selecting medical images for providing auxiliary diagnostic information taking into account the characteristics of the photographed subject (e.g., mainly the patient) and the type and grade of the disease index.

[0234] In another embodiment, the image analysis device (2000) can select a candidate image for calculating a disease index from a plurality of medical images and provide diagnostic support information based on the disease index obtained from the selected candidate image.

[0235] Hereinafter, a method for providing auxiliary diagnostic information based on a disease index acquired from a candidate image by the image analysis device (2000) will be described with reference to the drawings.

[0236] FIG. 23 is a flow chart that outlines a method for obtaining auxiliary diagnostic information from a candidate image.

[0237] Referring to FIG. 23, the method for the image analysis device (2000) to acquire diagnostic auxiliary information from candidate images may include the steps of acquiring a plurality of medical images (S1202), segmenting the plurality of medical images (S1212), determining candidate images from the plurality of segmented images (S1222), acquiring a disease index for the selected candidate image (S1402), and providing diagnostic auxiliary information based on the acquired disease index (S1602).

[0238] First, the image analysis device (2000) can acquire (S1202) multiple medical images from the image acquisition device (1000). The step of acquiring multiple medical images (S1202) is similar to the explanation in Figure 20, so a detailed explanation will be omitted.

[0239] Thereafter, the image analysis device 2000 can segment the plurality of medical images (S1212). The segmentation of the medical images can be performed in a similar manner to that described above, and therefore, a detailed description thereof will be omitted.

[0240] Once segmentation is performed, the image analysis device 2000 can determine candidate images from the segmented medical images (S1222). Specifically, the second controller 2002 can determine candidate images that meet predetermined criteria from the segmented medical images according to predetermined criteria. A detailed description of the candidate images will be provided below with reference to the accompanying drawings.

[0241] Once the candidate images are determined, the image analysis device (2000) can acquire a disease index from the candidate images (S1402). Specifically, the second controller (2002) can acquire a disease index for all or part of the candidate images using a program for calculating a disease index stored in the second memory (2002). The method for acquiring a disease index can be performed in a manner similar to that described above, and therefore a detailed description thereof will be omitted.

[0242] Once the disease index is acquired, the image analysis device (2000) can acquire and provide auxiliary diagnostic information based on the acquired disease index (S1602). Specifically, the second controller (2002) can provide auxiliary diagnostic information based on the disease index acquired from one or more candidate images. Here, the method by which the second controller (2002) provides auxiliary diagnostic information from multiple candidate images can be the same as the method of providing auxiliary diagnostic information from multiple medical images, as described with reference to FIGS. 20 to 22.

[0243] Hereinafter, an example in which candidate images are extracted from a plurality of medical images and an example in which a disease index is calculated from the extracted candidate images to provide auxiliary diagnostic information will be described with reference to the drawings.

[0244] FIG. 24 shows an example of candidate images according to an embodiment.

[0245] 24, the image analysis device 2000 can select candidate images. Specifically, the second controller 2002 can select one or more candidate images from among a plurality of medical images acquired from the image acquisition device 1000. Here, the candidate images are images that satisfy predetermined criteria among the plurality of medical images.

[0246] Here, the predetermined criterion may be determined to correspond to the disease index to be calculated. As an example, the predetermined criterion is whether or not the reference region is inclusive. That is, the second controller (2002) may select, as a candidate image, an image containing information about the reference region from among the plurality of medical images. As another example, the predetermined criterion is whether or not the target region is inclusive. That is, the second controller (2002) may select, as a candidate image, an image containing information about the target region from among the plurality of medical images. Furthermore, the second controller (2002) may select, as a candidate image, a medical image containing both the target region and the reference region.

[0247] In addition, the candidate image may be determined as one or more images included in a candidate image range, as shown in the drawings. Here, the candidate image range refers to a range from a first medical image satisfying a predetermined condition to a second medical image satisfying the predetermined condition among tomographic medical images having a consecutive order obtained by photographing the same object on various planes relative to one axis. Here, the range from the first medical image to the second medical image may include a plurality of medical images sequentially positioned between the first and second medical images.

[0248] That is, to explain a specific example with reference to the drawings, when the reference region is the ventricular region, among a plurality of medical images taken in successive cross sections of a transverse plane relative to the saggital axis, the range from the first medical image located at the bottom among the medical images including the ventricles to the second medical image located at the top among the medical images including the ventricles can be set as the candidate image range.

[0249] In other words, the second controller (2002) can select a candidate image or a range of candidate images based on whether the number of pixels corresponding to a predetermined condition contained in the medical image is less than or greater than a threshold value.

[0250] Specifically, the second controller (2002) can analyze the segmentation results of the medical image to determine whether the number of pixels labeled as ventricles is less than a threshold value. If the number of pixels labeled as ventricles contained in the medical image is greater than or equal to the threshold value, the second controller (2002) can determine the medical image as a candidate image.

[0251] In addition, when the second controller (2002) sets the range of candidate images, if the analysis result of two adjacent medical images shows that the number of pixels labeled as ventricles in one image is less than or equal to a critical value and the number of pixels labeled as ventricles in the other image is greater than or equal to the critical value, the other image can be set as the boundary of the range of candidate images.

[0252] FIG. 25 illustrates an example of providing diagnostic auxiliary information from a candidate image according to an embodiment.

[0253] 25, the image analysis device 2000 can provide auxiliary diagnostic information from a candidate image. Specifically, the second controller 2002 can obtain a disease index from the candidate image and provide auxiliary diagnostic information based on the obtained disease index.

[0254] The method for calculating the disease index is as described above, and therefore a detailed description thereof will be omitted.

[0255] As shown in FIG. 25, (a) shows a medical image that is not a candidate image, and (b) shows a medical image that has been determined as a candidate image.

[0256] In calculating the disease index according to this embodiment, it is important that a specific region of the imaged subject is included in the medical image. For example, the method for obtaining the disease index according to the embodiment can be derived based on the correlation between the ventricles and WMH. However, if the ventricles are not included in the medical image, it may be difficult to calculate the disease index, and accurate diagnostic auxiliary information may not be provided. That is, in the case of (a) of Figure 25, which is a medical image of the upper end of the brain, the ventricle region is not included in the medical image, so the method for calculating the disease index according to the embodiment may not be applicable.

[0257] In the case of (b), the ventricular region is included in the medical image, and the second controller (2002) can analyze the medical image such as (b) to calculate a disease index, and can obtain diagnostic auxiliary information based on the calculated disease index.

[0258] Furthermore, when analyzing all medical images of the subject, the image analyzer (2000) may have an excessive amount of calculation, slowing down the calculation speed and causing errors during the calculation process.

[0259] However, according to the method of providing diagnostic auxiliary information using candidate images of this embodiment, after segmenting multiple images, candidate images for calculating the disease index are first determined, and the disease index is calculated only for the determined candidate images. This reduces the amount of calculation, thereby increasing the calculation speed and improving the accuracy of the calculation.

[0260] FIG. 26 is another example of an implementation of providing auxiliary diagnostic information according to an embodiment.

[0261] 26, the image analyzing device 2000 according to the embodiment can provide auxiliary diagnostic information for at least one image among the candidate images. Specifically, the second controller 2002 can provide auxiliary diagnostic information for at least one image that satisfies a predetermined condition among the medical images selected as the candidate images.

[0262] Here, the predetermined condition can be determined taking into consideration the disease index. For example, the predetermined condition is a disease index value indicating the most severe disease progression. That is, the second controller (2002) can select a specific medical image, among the disease indexes of the candidate images, whose disease index indicates the most severe disease progression, as a medical image for providing auxiliary diagnostic information.

[0263] In addition, when multiple disease indexes can be calculated from a medical image, the predetermined condition may be determined by taking into consideration all of the disease indexes. That is, the second controller (2002) may select all of the specific candidate images for each of the multiple disease indexes as medical images for providing diagnostic auxiliary information, or may select only specific candidate images for some of the multiple disease indexes as medical images for providing diagnostic auxiliary information.

[0264] To explain a specific example with reference to the drawings, the second controller (2002) can provide auxiliary diagnostic information related to a medical image among a plurality of medical images that satisfies a predetermined condition related to a first disease index through the display module (2600). Here, the predetermined condition is whether the first disease index has the highest calculated grade among a plurality of candidate images. The second controller (2002) can also provide auxiliary diagnostic information related to a medical image among a plurality of medical images that satisfies a predetermined condition related to a second disease index through the display module (2600), and can also provide auxiliary diagnostic information for all medical images that satisfy the predetermined conditions related to the first and second disease indexes.

[0265] As described above, the method for determining a candidate image for calculating a disease index from a plurality of medical images and the method for calculating a disease index for one medical image have been mainly described.

[0266] However, in order to calculate a more accurate disease index, a plurality of medical images may be used to calculate the disease index, which will be described with reference to FIGS.

[0267] FIG. 27 is a flowchart illustrating a method for obtaining a disease index based on multiple images according to an embodiment.

[0268] Referring to FIG. 27, a method for obtaining a disease index based on multiple images according to an embodiment includes the steps of obtaining multiple medical images (S4200), performing segmentation on the multiple medical images (S4400), obtaining target region information of a first image (S4600), and obtaining a disease index of a second image based on the target region information of the first image (S4800).

[0269] First, the step of acquiring a plurality of medical images (S4200) and performing segmentation on the plurality of medical images (S4400) can be performed in a similar manner to the above-described operations, and therefore a detailed description thereof will be omitted.

[0270] stomach After performing segmentation on the medical image, the image analyzer (2000) can acquire information about a target region for the first image (S4600). Specifically, the second controller (2002) can acquire information about the target region corresponding to the disease index to be calculated. Here, the target region refers to an area that is essential for calculating a specific disease index. Specific details will be described later.

[0271] After acquiring information about the target area, the image analysis device (2000) can calculate a disease index for the second image based on the target area information of the first image. Specifically, the second controller (2002) can calculate a disease index related to the second image based on the target area information acquired from the first image and area information related to the photographed object included in the second image. Examples of this will be described later.

[0272] Hereinafter, a method for calculating a disease index of a second image in consideration of information of a first image will be described with reference to a specific example with reference to the accompanying drawings.

[0273] FIG. 28 is an example showing a process of calculating a disease index of a second image by taking into account information of a first image according to this embodiment.

[0274] According to this embodiment, the image analysis device (2000) can directly calculate the disease index of the second image from the first image. Specifically, the second controller (2002) can calculate the disease index of the second image based on the relationship between the first region information included in the first image and the second region information included in the second image. Here, the first region information is information about a reference region included in the first image, and the second region information is information about a target region included in the second image.

[0275] In order to calculate the disease index, there are cases where the medical image does not contain information about the reference region but does contain information about the target region. In such cases, the requirements for calculating the disease index are not sufficient, making it difficult to calculate the disease index. However, in some cases, it is necessary to calculate the disease index for such medical images.

[0276] 28, for example, the first slice image contains information about WMHs but does not contain information about the ventricles. In this case, it is difficult to derive a disease index derived from the relationship between the ventricles and WMHs from the first slice image. However, since WMHs are likely to contain important information about diseases, it is desirable to calculate a disease index for images in which WMHs are found in order to provide more accurate diagnostic auxiliary information.

[0277] In this case, the second controller (2002) can calculate the disease index of the second image based on the relationship between the first image and the second image, taking into account information about the first image. Here, the relationship between the first image and the second image can be acquired in advance. For example, the relationship between the first image and the second image is the distance between the slice images.

[0278] Further, specific examples will be described with reference to the drawings.

[0279] The second controller (2002) can first determine a first image as a result of segmentation of the plurality of medical images, the first image including a target region for calculating a disease index but not including a reference region within the medical image. For example, even if information about the ventricles and WMHs is required to calculate a disease index according to an embodiment, the second controller (2002) can select a first image including only information about WMHs but not information about the ventricles within the medical image.

[0280] Next, the second controller (2002) can select a second image from other medical images in the vicinity of the first image, the second image including a reference region not included in the first image. Here, the second image is an image immediately adjacent to the first image, but is not limited to this. For example, the second controller (2002) can select a second image including a ventricular region for calculating a disease index from medical images located in the periphery of the first image (e.g., within a predetermined distance from the first image in the saggital axis).

[0281] Once the second image is selected, the second controller (2002) can calculate a disease index based on the relationship between the reference region included in the second image and the target region included in the first image. For example, the second controller (2002) can calculate a disease index in the second image based on distance information from the ventricle region included in the second image to the WMH included in the first image and region information of the WMH included in the second image. Here, the distance between the first image and the second image can be acquired and stored in advance.

[0282] In addition, various criteria for deriving the relationship between the reference region included in the second image and the target region included in the first image may be determined. For example, the second controller (2002) may calculate a disease index based on the distance from the center of the ventricular region included in the second image to the WMH region included in the first image. For another example, the second controller (2002) may calculate a disease index based on the distance from the ventricular region included in the second image that is closest to the WMH region included in the first image to the WMH region included in the first image. Those skilled in the art will understand that various methods other than the above-described methods may be incorporated into the concept of the present invention as long as they are criteria that allow for calculating the distance from the ventricular region included in the second image to the WMH region included in the first image.

[0283] Hereinafter, a method for calculating a disease index of a second image indirectly from a first image will be described with reference to the drawings.

[0284] FIG. 29 shows another example of a process for calculating a disease index of a second image by taking into account information of a first image according to this embodiment.

[0285] According to this embodiment, the image analysis device (2000) can calculate the disease index of the second image indirectly from the first image. Specifically, the second controller (2002) can calculate the disease index of the second image based on the relationship between the reference cell in the second image, which is indirectly obtained from information about the reference area included in the first image, and the target area included in the second image.

[0286] A specific example will be described with reference to the drawings.

[0287] The second controller (2002) is as described in FIG. 27 and can first determine a first image as a result of segmentation of multiple medical images, which includes a target area for calculating a disease index within the medical image but does not include a reference area.

[0288] For example, the second controller (2002) may select a first image in which the medical image contains only information about the WMHs and no information about the ventricles, even though information about the ventricles and WMHs is required to calculate a disease index according to the embodiment.

[0289] Next, the second controller (2002) can select a second image from other medical images adjacent to the first image that includes a reference region not included in the first image. For example, the second controller (2002) can select a second image from medical images located near the first image that includes a ventricle region for calculating a disease index.

[0290] When the second image is selected, the second controller (2002) can determine a reference cell on the first image that corresponds to the reference area included in the second image based on information about the reference area included in the second image. For example, the second controller (2002) can determine a reference cell on the first image that corresponds to the ventricular area included in the second image based on information about the ventricular area included in the second image.

[0291] Once the reference cell is determined, the second controller (2002) can calculate a disease index based on information about the reference cell and the target region included in the first image. That is, for example, the second controller (2002) can calculate a disease index by considering the reference cell of the second region corresponding to the ventricular region included in the first image as the ventricle and deriving the relationship with the WMH included in the first image. Of course, a disease index calculation method similar to that described above can be applied to the method of calculating a disease index based on the relationship between the reference cell and the WMH.

[0292] As described above, according to this embodiment, the image analysis device (2000) calculates a disease index using multiple medical images to obtain an accurate disease index, and can provide diagnostic auxiliary information based on this.

[0293] In addition, the diagnostic assistance information providing system (10000) according to the embodiment can calculate disease indexes even for 3D medical models that are 3D modeled from medical images, and provide diagnostic assistance information.

[0294] Hereinafter, a method for providing auxiliary diagnostic information based on a 3D medical model will be described with reference to FIGS.

[0295] FIG. 30 is a flowchart illustrating an example of a method for providing diagnostic auxiliary information based on a 3D medical model according to an embodiment.

[0296] According to this embodiment, the method for providing diagnostic auxiliary information based on a 3D medical model performed by the image analysis device (2000) includes the steps of acquiring a plurality of medical images (S5200), performing segmentation on the plurality of medical images (S5400), performing 3D modeling based on the segmented medical images (S5600), and acquiring a disease index based on the 3D modeling (S5800).

[0297] The step of acquiring a plurality of medical images (S5200) and the step of performing segmentation on the plurality of medical images (S5400) may be performed in a manner similar to that described above, and detailed description thereof will be omitted.

[0298] After performing segmentation on the plurality of medical images, the image analysis device (2000) can acquire a 3D medical model based on the plurality of segmented medical images (S5600). Specifically, the second controller (2002) can generate a 3D medical model by processing the plurality of medical images obtained by photographing the subject at successive cross sections. Here, the 3D medical model is generated based on the plurality of segmented medical images and can include 3D pixel information reflecting information on a plurality of brain regions. That is, the brain can be segmented into a plurality of brain regions in three dimensions. In addition, the reference boundary described above can be formed in three dimensions.

[0299] When the 3D medical model is acquired, the image analysis device (2000) can acquire a disease index based on the acquired 3D medical model and provide auxiliary diagnostic information. Specifically, the second controller (2002) can calculate a disease index from the 3D medical model using a program for acquiring a disease index stored in the second memory (2400) and acquire auxiliary diagnostic information from the calculated disease index.

[0300] An example of calculating a disease index from a 3D medical model to provide diagnostic auxiliary information will be described below with reference to the drawings.

[0301] FIG. 31 shows an example of a process for calculating a disease index from a 3D medical model according to this embodiment, and FIG. 32 shows another example of a process for calculating a disease index according to this embodiment.

[0302] Referring to FIG. 31, the image analysis device (2000) can obtain a disease index based on region information included in a 3D medical model. Specifically, the second controller (2002) can calculate a disease index based on information on one or more specific regions of an imaged object included in the 3D medical model. Here, the one or more specific regions can include a target region and a reference region, as described above. Specifically, the target region or reference region is a cell (e.g., a voxel) labeled as a target region or a reference region expressed in three dimensions. In addition, the second controller (2002) can set a criterion for deriving a relationship between regions included in one or more imaged objects.

[0303] As an example, the second controller (2002) can calculate a disease index based on the ventricular region and the WMH region included in the 3D medical model. Here, the second controller (2002) can set a reference boundary to derive the relationship between the ventricular region and the WMH region, similar to the above example. That is, the second controller (2002) can set a set of cells a predetermined distance away from the ventricular region in three dimensions as the reference boundary. Also, similar to the reference boundary set in the medical image described above, the second controller (2002) can set or modify the reference boundary in various ways.

[0304] The second controller (2002) can calculate a disease index based on the reference boundary formed in three dimensions, taking into account the relationship with the WMH region. Here, the relationship between the reference boundary and the WMH is similar to that described above. Furthermore, by performing three-dimensional 3D modeling on two-dimensional medical images, information about the region of the imaged object, such as region information about the target region, becomes a three-dimensional physical quantity. That is, the second controller (2002) can also obtain three-dimensional information, such as the thickness of a specific region included in the imaged object, which cannot be obtained from two-dimensional medical images, through 3D modeling. As another example, information about the WMH is the volume (or number) corresponding to cells tagged in the WMH.

[0305] 32, the image analysis device 2000 can calculate a disease index based on a plurality of planes included in the 3D medical model and provide auxiliary diagnostic information. Specifically, the second controller 2002 can calculate a disease index based on one or more planes including information on at least one specific region of the photographed object included in the 3D medical model and provide auxiliary diagnostic information.

[0306] Performing 3D modeling has the advantage of making it easier to derive relationships between specific regions for calculating disease indexes. That is, as described above, when calculating disease indexes by analyzing slice images, if the slice images do not contain information about the required regions included in the photographed object for calculating disease indexes, not only is it difficult to obtain the disease indexes, but the accuracy of the obtained disease indexes may be reduced.

[0307] However, when performing 3D modeling to calculate the disease index, the accuracy of the disease index can be improved by determining a plane that includes all the areas required for calculating the disease index and then calculating the disease index for that plane.

[0308] A specific example will be described with reference to the drawings.

[0309] Referring again to FIG. 32, the second controller (2002) may set a first plane corresponding to a preset criterion so that the ventricular region and the first WMH region are both included, and calculate a disease index for the set first plane. Here, there are various criteria for setting the first plane. For example, the first plane is set in consideration of the distance between the target region and the reference region. As a more specific example, the first plane is set to include a straight line that minimizes the distance between the WMH region and the ventricular region.

[0310] As another example, the second controller (2002) can set a second plane taking into account the number of target regions included in the plane and calculate a disease index for the set second plane. For example, the second controller (2002) can set the second plane to include the largest number of target regions in addition to including information about the reference region. The second controller (2002) can also set the second plane to maximize the number of labeled pixels in the target regions.

[0311] That is, the second controller (2002) can set a first plane to include a first target area and a reference area according to various criteria, and can set a second plane to include a second target area and a reference area. At this time, the first plane and the second plane can form a predetermined angle. In this way, the image analysis device (2000) can derive the relationship between the reference area and the target area even for a plane that forms a predetermined angle with the plane including the medical image that is the basis of the 3D medical model, and can calculate a disease index or provide auxiliary diagnostic information.

[0312] The above describes an example of a method for obtaining a disease index from one or more medical images executed by the diagnostic auxiliary information providing system (10000) according to various embodiments, and providing diagnostic auxiliary information based on the obtained disease index.

[0313] As described above, according to the method for providing diagnostic auxiliary information executed by the diagnostic auxiliary information providing system (10000) of this embodiment, by calculating a disease index from a medical image based on an automated algorithm or program having clear and uniform criteria, subjective human judgment can be eliminated and a more objective disease index can be calculated, thereby providing more accurate diagnostic auxiliary information.

[0314] Hereinafter, a method for the diagnostic assistance information providing system (10000) according to the embodiment to perform segmentation on a specific region contained in a medical image from a medical image will be described.

[0315] According to this embodiment, the diagnostic assistance information providing system 10000 can segment a medical image. Specifically, the image analysis device 2000 can acquire a medical image from the image acquisition device 1000 and segment the acquired medical image. More specifically, the second controller 2002 can segment the medical image using an algorithm for image segmentation stored in the second memory 2400.

[0316] The following description will be made with reference to the drawings.

[0317] FIG. 33 generally illustrates the medical image segmentation operation performed by the video analyzer according to this embodiment.

[0318] According to this embodiment, the image analysis device 2000 can receive input data and output output data. Specifically, the second controller 2002 can input the medical image acquired from the image acquisition device 1000 as input data to an algorithm for image segmentation, and can obtain output data in which the medical image is segmented.

[0319] The image analysis device 2000 according to the embodiment can use various algorithms for image segmentation.

[0320] For example, an algorithm for image segmentation can be provided by a machine learning model. A typical example of a machine learning model is an artificial neural network. Specifically, a typical example of an artificial neural network is a deep learning-based artificial neural network that includes an input layer that accepts data input, an output layer that outputs the results, and a hidden layer that processes data between the input layer and the output layer.

[0321] Specific examples of artificial neural networks include convolutional neural networks, recurrent neural networks, and deep neural networks. In this specification, the term "artificial neural network" should be interpreted in a comprehensive sense to include the above-mentioned artificial neural networks, various other types of artificial neural networks, and artificial neural networks that are combinations of these, and must be a deep running series.

[0322] Furthermore, the machine learning model does not necessarily have to be in the form of an artificial neural network model, but may also include other techniques such as nearest neighbor algorithm (KNN), random forest, support vector machine (SVM), principal component analysis (PCA), etc., as well as ensembles of the above-mentioned techniques and combinations thereof in various ways. Meanwhile, in embodiments that are described primarily with an artificial neural network, it is clear that the artificial neural network may be replaced with a different machine learning model unless otherwise specified.

[0323] Furthermore, in this specification, the algorithm for image segmentation is not necessarily limited to a machine learning model, that is, the algorithm for image segmentation may include various judgment / decision algorithms other than a machine learning model.

[0324] Therefore, it should be understood that in this specification, an algorithm for image segmentation has a comprehensive meaning that includes all types of algorithms that analyze a medical image and distinguish regions contained within the medical image.

[0325] An example implementation of the artificial neural network model will now be described with reference to FIGS.

[0326] FIG. 34 illustrates an example implementation of an artificial neural network model according to an embodiment.

[0327] Referring to FIG. 34, the image analysis device 2000 according to an embodiment of the present invention can utilize U-net, an artificial neural network for image segmentation.

[0328] U-net, which is often used for image segmentation, can be constructed with an architecture that includes a contraction path and an expansion path.

[0329] Specifically, the U-net's contraction path can be configured to perform two convolutions and max pooling consecutively, allowing image-related features to be extracted from the U-net's contraction path.

[0330] However, since the contraction path also reduces the size of the feature map, the U-net can be configured to include an additional expansion path to restore the size of the feature map.

[0331] The U-net extension path can be configured to perform up-convolution and two-degree convolution consecutively, and the size of the image and feature map can be extracted in the U-net extension path.

[0332] Additionally, the U-net is architectured to concatenate feature maps at the same level, allowing the contraction path to provide location information about the feature to the expansion path.

[0333] At this time, based on the label difference between the input image label and the output segmentation map, the parameters or weight values ​​of at least one node in the layer containing the U-net are adjusted so that the label difference between the input image label and the output segmentation map is minimized.

[0334] FIG. 35 illustrates another implementation of an artificial neural network model according to an embodiment.

[0335] 35, the video analysis device 2000 according to the embodiment can utilize U-net++ as an artificial neural network for image segmentation. U-net++ is an artificial neural network model that uses the dense block idea of ​​DenseNet to improve the performance of U-net, and differs from U-net in that a convolution layer exists in the skip path to bridge the semantic gap between the feature maps of the encoder and decoder, and a tense skip connection exists in the skip path to improve gradient flow.

[0336] Specifically, the image analysis device 2000 can be implemented to input an input image to the input layer of a U-net++ neural network model and obtain label information output through the output layer. At this time, the image analysis device 2000 can adjust the parameter or weight value of at least one node in a hidden layer included in the U-net++ based on the difference between the label information included in the input image and the label information output from the neural network model.

[0337] More specifically, the second controller (2002) is configured to repeatedly perform the operation of adjusting the parameters or weights of at least one of the nodes described above, and can obtain the parameters or weights of the nodes that minimize the difference between the label information included in the input image and the label information output from the neural network model.

[0338] FIG. 36 illustrates segmentation results using an artificial neural network model according to an embodiment.

[0339] According to an embodiment, the video analysis device 2000 can segment the medical image based on the characteristics of the medical image. Specifically, the second controller 2002 can segment the medical image to correspond to the conditions under which the medical image was acquired.

[0340] Here, the conditions under which the medical image is acquired may vary depending on the setting parameters of the image acquisition device 1000. For example, the conditions under which the medical image is acquired may be preset magnetic state parameter values ​​of the image acquisition device 1000. Specifically, if the image acquisition device 1000 is implemented as an MRI device, a T1-weighted image or a T2-weighted image may be acquired depending on the setting of the TR / TE value, and the second controller 2002 may segment the image to correspond to the acquired image. Here, the characteristics according to the acquisition conditions of the medical image are as described above.

[0341] A specific example will be described with reference to the drawings.

[0342] Referring to FIG. 36, segmentation results of a T1-weighted image and a T2-FLAIR image acquired from the same subject are shown.

[0343] According to an embodiment, the second controller (2002) can segment the T1-weighted image. Here, the T1-weighted image can be segmented to determine anatomical characteristics. As shown in the drawing, the segmentation result of the T1-weighted image shows a clearer segmentation of wrinkles in the gray matter outer shell region compared to the segmentation result of the T2-FLAIR image.

[0344] Additionally, according to an embodiment, the second controller (2002) can segment the T2-FLAIR image. Here, the T2-FLAIR image can be segmented to determine pathological characteristics. As shown in the drawings, the segmentation results of the T2-FLAIR image contain more information about the WMH region than the segmentation results of the T1-weighted image.

[0345] In this way, even if medical images are acquired from the same subject, the characteristics (e.g., anatomical characteristics, pathological characteristics) contained in the medical image may change depending on the acquisition conditions of the image acquisition device (1000), and the information observable in the medical image may also change depending on the characteristics of the medical image.

[0346] However, if necessary, more accurate diagnostic support information can be provided if various medical image characteristics are comprehensively considered. Therefore, the following describes a process for segmenting a medical image to include multiple characteristics.

[0347] FIG. 37 shows an example of a medical image segmented to include multiple features according to an embodiment.

[0348] 37, the image analysis device 2000 according to the embodiment can segment a medical image so that at least two or more characteristics that change depending on the acquisition conditions are included in one medical image. Specifically, the second controller 2002 can segment the first image based on the first image so that the characteristics of the second image are included.

[0349] A specific example will be described with reference to the drawings.

[0350] The second controller (2002) can input a first medical image including a first characteristic as input data to the artificial neural network to obtain output data labeled with the first characteristic and a second characteristic related to the second medical image. Here, the artificial neural network can learn based on a learning set related to the second image having the second characteristic. For example, the first characteristic may be an anatomical or structural characteristic, thereby indicating that the first image is a T1-weighted image, and the second characteristic may be a pathological characteristic, thereby indicating that the second image is a T2-FLAIR image.

[0351] As a more specific example, in conjunction with (a), the second controller (2002) can input a first image (in the illustrated example, a T2-FLAIR image) containing pathological characteristics as input data to an artificial neural network according to the embodiment.

[0352] Here, if an artificial neural network trained only on the learning set for the first image is used, the second controller (2002) will output only segmentation results that include only the first feature (i.e., pathological feature) along with (b).

[0353] However, the artificial neural network according to an embodiment of the present invention can be trained using a learning set based on both the first image related to the first characteristic and the second image related to the second characteristic. Thus, the second controller (2002) can input the first image into the artificial neural network and obtain a segmentation result for the first image that reflects both the first characteristic and the second characteristic, as well as (c).

[0354] In this way, even when segmenting a medical image containing one characteristic, information about multiple characteristics can be obtained together, making it possible to obtain various disease-related information from one medical image, and the image analysis device (2000) according to the embodiment can provide more accurate and various diagnostic auxiliary information from the medical image.

[0355] Hereinafter, the learning process of an artificial neural network according to an embodiment will be described with reference to the accompanying drawings.

[0356] FIG. 38 is a flowchart illustrating the learning process of an artificial neural network according to an embodiment.

[0357] The artificial neural network according to the embodiment may be trained using training data. Here, the artificial neural network according to the embodiment may be trained through various devices capable of driving an artificial neural network algorithm. As an example, the artificial neural network may be trained through a video analysis device (2000). For convenience of explanation, the present specification describes the artificial neural network being trained through a video analysis device (2000), but it should be understood that the present specification is not limited thereto and that the artificial neural network may be trained through other devices for driving an artificial neural network.

[0358] Referring to FIG. 39, the learning process of the artificial neural network according to the embodiment includes a step of segmenting a first image including a first characteristic (S6000), a step of aligning the segmentation information of the first image based on a second image including a second characteristic (S6200), a step of performing primary learning using the aligned image and the second image (S6400), a step of morphologically correcting the second image including the output result of the primary learning (S6600), and a step of performing secondary learning using the second image including the morphologically corrected segmentation result (S6800).

[0359] First, the image analysis device 2000 can segment a first image including a first characteristic (S6000). Specifically, the second controller 2002 can segment the first image to include information about the first characteristic acquired under a first acquisition condition. In other words, the image acquisition device 1000 can segment the first image including the first characteristic acquired when set to a first parameter.

[0360] After segmenting the first image, the image analysis device (2000) can match the segmented first image with the second image based on the second image (S6200). Specifically, the second controller (2002) can match the first image segmented to include the first characteristic with the second image including the second characteristic to obtain a second image including segmented information related to the first characteristic. In other words, the first image segmented to include the first characteristic can be matched with the second image including the second characteristic obtained when the second parameter of the image acquisition device (1000) is set. This will be described in more detail below.

[0361] After the first image and the second image are aligned, the video analysis device (2000) can perform primary learning using the aligned second image and the unaligned second image (S6400). Specifically, the second controller (2002) can train the artificial neural network using the aligned second image to reflect the first characteristic, so that the artificial neural network according to the embodiment outputs a segmentation result including the first characteristic from the second image.

[0362] That is, the artificial neural network can be trained using a learning set including a second image labeled with a first characteristic and an original second image. Thus, the artificial neural network can receive the second image as input data and output the second image labeled with the first characteristic. While the learning set has been described as including the original second image, processed second images other than the original second image can also be used. For example, the second image can be a second image segmented to include the second characteristic. It can also be a second image that has undergone other preprocessing processes. However, for the sake of convenience, the following description will primarily discuss the artificial neural network training using a running set based on the original second image.

[0363] After the artificial neural network has been primarily trained, the image analysis device (2000) can perform morphological correction on the output image of the primarily trained artificial neural network (S6600). Specifically, the second controller (2002) can input the original second image data into the artificial neural network and perform morphological correction on the second image segmented to include the second feature output, in order to improve the accuracy of the segmentation result.

[0364] The first trained artificial neural network may not accurately contain information about the first characteristic. Therefore, the image analysis device (2000) according to the embodiment can improve the computational accuracy of the artificial neural network by morphologically correcting the first output data of the artificial neural network based on the first image containing the first characteristic, so that the artificial neural network can more accurately distinguish information about the first characteristic on the second image.

[0365] Once the morphological correction of the primary output data of the artificial neural network is complete, the image analysis device (2000) can secondarily train the artificial neural network based on the morphologically corrected second image (S6800). Specifically, the second controller (2002) can train the artificial neural network according to the embodiment based on a learning set including the second image that has been morphologically corrected and labeled with the first characteristic. That is, the artificial neural network according to the embodiment can perform segmentation from the second image so that information related to the first characteristic is more accurately reflected by training using a learning set including the second image that has been labeled to reflect the morphologically corrected first characteristic.

[0366] Hereinafter, an example of a learning process of an artificial neural network according to an embodiment will be described with reference to the accompanying drawings.

[0367] FIG. 39 illustrates an example of a process for matching a first image with a second image according to an embodiment.

[0368] Referring to Figure 39, the image analysis device (2000) according to the embodiment can match a first image and a second image. Specifically, the second controller (2002) can obtain a match matrix between the first image and the second image. Here, the match matrix refers to a transformation function between the first image and the second image. That is, the match matrix according to the embodiment can be expressed as a function that represents the correlation between a first point included in the first image and a second point included in the second image, with respect to the first image and the second image of the same photographed object. Here, the first point and the second point may refer to a pixel or a set of pixels.

[0369] The image analysis device 2000 according to the embodiment can process a second image to correspond to the segmentation result of the first image based on the first image segmented with respect to the first characteristic using a matching matrix. That is, the second controller 2002 can segment the second image so that the segmentation result of the first image with respect to the first characteristic is reflected in the second image. Here, as described above, the second image can be pre-segmented to include the second characteristic. That is, the second controller 2002 can segment the second image so that both the first characteristic and the second characteristic are reflected.

[0370] As a more specific example, the first image may be a T1-weighted image and the second image may be a T2-FLAIR image, that is, the image analysis device 2000 may first align the T1-weighted image and the T2-FLAIR image to obtain a matching matrix.

[0371] In addition, the image analysis device (2000) can segment the T1-weighted image so that the anatomical and structural characteristics of the T1-weighted image are reflected. For example, the second controller (2002) can process the T1-weighted image so that specific brain regions can be distinguished. More specifically, the second controller (2002) can segment the brain so that organs contained in the brain, such as the cerebrum, cerebellum, diencephalon, and hippocampus, can segment the T1-weighted image so that organs contained in the brain, such as a seahorse, can segment the brain so that each part of the brain, such as the temporal lobe, frontal lobe, and occipital lobe, can be distinguished, or a combination of these can be segmented.

[0372] The image analyzer (2000) can then use the matching matrix to match the segmented T1-weighted image to the T2-FLAIR image so that the segmentation results of the T1-weighted image are reflected in the T2-FLAIR image.

[0373] As a result, the image analyzer 2000 can acquire a T2-FLAIR image segmented to reflect anatomical characteristics. Specifically, the second controller 2002 can segment the T2-FLAIR image so that the above-mentioned anatomical characteristics are reflected in a T2-FLAIR image that distinguishes between WMHs or white matter and gray matter, which are materials that make up the brain, observable in medical images. The T2-FLAIR image segmented to reflect the above-mentioned anatomical characteristics can be used for the primary learning of the artificial neural network, as described above.

[0374] FIG. 40 shows an example of morphological modification according to an embodiment.

[0375] 40, the image analysis device 2000 according to the embodiment can morphologically modify the segmentation result for a medical image. Specifically, the second controller 2002 can morphologically modify the segmentation result to reflect a first characteristic included in the second image. The second image including the morphologically modified segmentation result can more accurately reflect the first characteristic.

[0376] A specific example will be described with reference to the drawings.

[0377] As described above, the artificial neural network can perform primary training using a T2-FLAIR image segmented to reflect anatomical characteristics and the original T2-FLAIR image. As a result of the primary training, the artificial neural network can receive a T2-FLAIR image as input and output a T2-FLAIR image segmented to reflect the characteristics (anatomical or structural characteristics) of the T1-weighted image. The T2-FLAIR image output by the artificial neural network is segmented to reflect the characteristics of the T1-weighted image, but some areas are incomplete and require correction.

[0378] In case (a), the first learning result of the artificial neural network according to the embodiment is shown, in which the outer portion of the segmented area is segmented so as to incompletely reflect the anatomical characteristics, while in case (b), the first output of the morphologically corrected artificial neural network is shown, in which the outer portion of the segmented area is corrected to better reflect the anatomical characteristics.

[0379] More specifically, in (a), some gray matter regions are not distinguished, and the boundaries between regions are partially broken. There is also noise due to incorrectly labeled regions caused by incomplete learning.

[0380] However, the morphologically corrected medical image shows well-defined gray matter regions, with the boundaries of each region clearly distinguishing specific regions.

[0381] According to this embodiment, the second controller (2002) can perform morphological correction based on the characteristics of the brain structure. Generally, brain regions including the ventricles must be connected in 26 different ways in three dimensions. Therefore, the second controller (2002) can reduce noise contained in the segmented medical image based on the connectivity between each brain region.

[0382] In addition, the ventricles are mistakenly distinguished from other non-tissues and are mistakenly labeled as non-tissues. In this case, to compensate for the mistaken labeling of the ventricular regions, the second controller (2002) can label the ventricular regions using a fill-hole method.

[0383] In this way, the artificial neural network according to the embodiment can be retrained by inputting morphologically modified segmentation results and outputting results that more accurately reflect the characteristics that are intended to be included.

[0384] FIG. 41 is a flowchart illustrating a deploying process using an artificial neural network of a video analysis device according to an embodiment.

[0385] Referring to FIG. 41, the deployment process using an artificial neural network of the image analysis device (2000) according to the embodiment includes a step of inputting a first image including a first characteristic (S7000), and a step of outputting the first image processed to include a segmentation result including the first characteristic and a second characteristic (S7200).

[0386] First, the image analysis device (2000) can input a first image including a first characteristic to the artificial neural network (S7000). Specifically, the second controller (2002) can input the first image including the first characteristic to the artificial neural network that has been fully trained and stored in the second memory (2400). Here, the artificial neural network has been fully trained through the above-described training process.

[0387] When a first image is input, the image analysis device (2000) can obtain, as an output result of the artificial neural network, a processed first image that includes a first characteristic associated with the first image and a second characteristic associated with a second image of a different format from the first image (S7200). Specifically, the second controller (2002) can obtain a segmented first image such that the first characteristic associated with the first image and the second characteristic associated with the second image of a different format from the first image are comprehensively distinguished.

[0388] By using an artificial neural network that has been fully trained according to the artificial neural network training process of the embodiment, the image analysis device (2000) can obtain a medical image that is segmented to include both the first characteristic and a second characteristic different from the first characteristic, even if the medical image includes only the first characteristic, thereby obtaining various information for analyzing a disease for which related information is desired, and providing a variety of information related to the disease to the user by analyzing the various information.

[0389] FIG. 42 shows an example of the final output result of an artificial neural network according to an embodiment.

[0390] 42, the image analysis device 2000 can input a first image to an artificial neural network to obtain a first image that has been processed to include various characteristics. Specifically, the second controller 2002 can input the first image that reflects the first characteristic to the artificial neural network to obtain a first image that has been segmented to more fully reflect characteristics different from the first characteristic.

[0391] To explain a specific example with reference to the drawings, the image analysis device (2000) can input a T2-FLAIR image into an artificial neural network to obtain an image processed to reflect the characteristics of a T1-weighted image.

[0392] Specifically, the second controller (2002) can input a T2-FLAIR image to a fully trained artificial neural network based on a learning set that reflects the characteristics of a T1-weighted image and a T2-FLAIR image, thereby obtaining a T2-FLAIR image segmented to reflect the characteristics of the T1-weighted image. Naturally, the artificial neural network is trained to perform segmentation based on the existing characteristics of the T2-FLAIR image. That is, the artificial neural network according to this embodiment can use a T2-FLAIR image as input data to obtain a segmented image that not only reflects anatomical or structural characteristics that are easily observable in a T1-weighted image, but also includes all lesion characteristics that are easily observable in a T2-FLAIR image.

[0393] To give a more specific example, the final output of the artificial neural network according to the embodiment may be segmented to include all of the characteristics of the T1-weighted image, along with the organs and locations of brain regions, based on a T2-FLAIR image that clearly distinguishes between pathological characteristics such as WMH and brain materials such as white matter and gray matter.

[0394] Methods according to embodiments may be embodied in the form of program instructions executable by various computer means and recorded on a computer-readable recording medium. The computer-readable recording medium may include, alone or in combination, program instructions, data files, data structures, and the like. The program instructions recorded on the medium may be specially designed and constructed for the embodiments, or may be publicly known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include not only machine language code produced by a compiler, but also high-level language code executable by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and may function in the same manner.

[0395] While the present invention has been described above by way of limited examples and drawings, those skilled in the art will appreciate that various modifications and variations may be made based on the above description, including, for example, performing the described techniques in a different order than described, and / or combining or combining the described system, structure, device, circuit, or other components in a different manner than described, or replacing or substituting other components or equivalents, while still achieving suitable results.

[0396] Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the present invention.

Claims

1. A method for providing auxiliary diagnostic information performed by an auxiliary diagnostic information providing device, comprising: acquiring a plurality of images relating to the brain; selecting one or more candidate images from the plurality of images, the candidate images including information related to ventricular regions and white matter hyperintensity (WMH) regions; obtaining a disease index from the one or more candidate images; Considering a plurality of disease indices calculated from the one or more candidate images, selecting a target medical image corresponding to a disease index among the plurality of disease indices that satisfies a predetermined condition; outputting auxiliary diagnostic information related to the target medical image; Including, The step of selecting one or more candidate images comprises: segmenting the brain-related images to obtain information about a plurality of brain regions within the brain-related images; detecting the brain regions corresponding to the ventricular regions and the white matter hyperintensity signal regions; and determining, from the brain-related images, an image that includes all of the ventricular regions and the white matter hyperintensity signal regions as the candidate image; The step of obtaining the disease index includes: determining at least one of the width or extent of a white matter hyperintensity signal region included in the candidate image; determining the distance between the ventricular region and the white matter hyperintensity signal region; and calculating a disease index taking into account the width or extent of the white matter hyperintensity signal region and the distance between the ventricular region and the white matter hyperintensity signal region, The diagnostic auxiliary information is obtained from the disease index calculated from the target medical image. A method for providing diagnostic auxiliary information, comprising:

2. The target medical image corresponding to the disease index that satisfies the predetermined condition is the medical image that shows the highest disease index grade value.

2. The method for providing auxiliary diagnostic information according to claim 1.

3. The disease index includes a first disease index and a second disease index.

2. The method for providing auxiliary diagnostic information according to claim 1.

4. The target medical image corresponding to the disease index satisfying the predetermined condition is a medical image in which either one of the first disease index and the second disease index indicates the highest disease index grade value.

4. The method for providing auxiliary diagnostic information according to claim 3.

5. The target medical image corresponding to the disease index that satisfies the predetermined condition is the medical image with the highest grade values ​​of both the first and second disease indexes.

4. The method for providing auxiliary diagnostic information according to claim 3.

6. The disease index further includes a third disease index; The third disease index is obtained by considering the first and second disease indexes.

4. The method for providing auxiliary diagnostic information according to claim 3.

7. The target medical image corresponding to the disease index that satisfies the predetermined condition is the medical image with the highest grade value of the third disease index.

7. The method for providing auxiliary diagnostic information according to claim 6.

8. The target medical image corresponding to the disease index that satisfies the predetermined condition is the medical image in which the width or extent of the white matter hyperintensity signal region is measured to be the largest.

2. The method for providing auxiliary diagnostic information according to claim 1.

9. further comprising the step of outputting auxiliary diagnostic information obtained from the target medical image; The diagnostic auxiliary information includes a width or extent value of the white matter hyperintensity region.

9. The method for providing auxiliary diagnostic information according to claim 8.

10. In the diagnostic auxiliary information providing device, a communication module for acquiring a plurality of medical images; a controller for analyzing the plurality of medical images; a display module for outputting analysis results for the plurality of medical images; the controller selects one or more candidate images from the plurality of medical images, the candidate images including all information related to ventricles and white matter hyperintensities (WMH); the controller acquires a disease index from the one or more candidate images, determines regional information of white matter hyperintensity signal regions included in the one or more candidate images, determines a distance between the ventricle and the white matter hyperintensity signal region, and calculates a disease index taking into account the regional information of the white matter hyperintensity signal region and the distance between the ventricle and the white matter hyperintensity signal region; the controller considers a plurality of disease indices calculated from the one or more candidate images and selects a target medical image corresponding to a disease index that satisfies a predetermined condition from the plurality of disease indices; The display module is said disease index calculated from said target medical image; outputting auxiliary diagnostic information obtained from A diagnostic auxiliary information providing device characterized by:

11. The target medical image corresponding to the disease index that satisfies the predetermined condition is the medical image that shows the highest disease index grade value.

11. The diagnostic auxiliary information providing device according to claim 10.

12. The disease index includes a first disease index and a second disease index.

11. The diagnostic auxiliary information providing device according to claim 10.

13. The target medical image corresponding to the disease index satisfying the predetermined condition is a medical image in which either one of the first disease index and the second disease index indicates the highest disease index grade value.

13. The diagnostic auxiliary information providing device according to claim 12.

14. The target medical image corresponding to the disease index that satisfies the predetermined condition is the medical image with the highest grade values ​​of both the first and second disease indexes.

13. The diagnostic auxiliary information providing device according to claim 12.

15. The disease index further includes a third disease index; The third disease index is obtained by considering the first and second disease indexes.

13. The diagnostic auxiliary information providing device according to claim 12.

16. The target medical image corresponding to the disease index that satisfies the predetermined condition is the medical image with the highest grade value of the third disease index.

16. The diagnostic auxiliary information providing device according to claim 15.

17. The target medical image corresponding to the disease index that satisfies the predetermined condition is the medical image in which the width or extent of the white matter hyperintensity signal region is measured to be the largest.

11. The diagnostic auxiliary information providing device according to claim 10.

18. The controller outputs auxiliary diagnostic information obtained from the target medical image via the display module; The diagnostic auxiliary information includes a width or extent value of the white matter hyperintensity region.

18. The diagnostic auxiliary information providing device according to claim 17.

19. A computer-readable recording medium having a program recorded thereon for carrying out the method according to any one of claims 1 to 9.

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