Image processing device, method for operating image processing device, and program

Abstract

To provide an image processing device, a method for operating the image processing device, and a program, in which a plurality of images acquired at different times can be preferably compared.SOLUTION: An image processing device (16): receives (30) a plurality of first images generated by imaging a subject and to each of which a plurality of various first imaging conditions are applied; designates (32) a first area of interest in the plurality of first images; acquires (36) measurement information related to a second image having a second area of interest corresponding to the first area of interest in the plurality of second images generated by imaging the subject prior to the imaging of the first image; specifies (38) the first image contained in a range of measurement information from the plurality of first images; and measures (40) the first area of interest specified in the first image.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an image processing device, an operating method for the image processing device, and a program. [Background technology]

[0002] Generally, when observing the progression and healing status of a lesion in a patient's body, After a certain period of time has passed since the image of the lesion was acquired, the image of the lesion is again acquired. The images are then compared and interpreted on two different time-series images.

[0003] Patent Document 1 discloses a method for extracting corresponding regions of interest from two or more images to be compared for the same subject. An image processing method is described in which the same image processing is performed on both regions of interest.

[0004] The image processing method described in the document provides accurate ratios for regions of interest in multiple images to be compared. This enables comparative interpretation, improving the interpretation performance of regions of interest that correspond to each other. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-63563 Summary of the Invention [Problem to be solved by the invention]

[0006] For example, when comparatively reading tumor images, comparison of tumor size is the most important factor. To accurately compare the sizes of two tumors to be compared, tumor measurements in the current examination must be performed under the same conditions as or similar to those of the images used in the previous examination. However, it is time-consuming to determine the image conditions to be used in the current examination according to the image conditions used in the previous examination.

[0007] The method described in Patent Document 1 does not solve the above-mentioned problem because it is difficult to compare and interpret the image used in the past examination with the image used in the current examination if the conditions for the image used in the past examination and the image used in the current examination are not the same.

[0008] The present invention has been made in view of the above circumstances, and has as its object to provide an image processing device, an operating method for an image processing device, and a program that can realize a preferable comparison of a plurality of images acquired at different times. [Means for solving the problem]

[0009] An image processing device according to a first aspect of the present disclosure includes one or more processors and one or more memories for storing instructions to be executed by the one or more processors, wherein the one or more processors receive a plurality of first images generated by photographing a subject, each of which is subjected to a plurality of different first image conditions; specify a first region of interest in the plurality of first images; acquire measurement information for a plurality of second images generated by photographing the subject before photographing the first images, the second images having a second region of interest corresponding to the first region of interest; identify, from the plurality of first images, a first image that falls within the range of the measurement information; and measure the first region of interest in the identified first image.

[0010] According to the image processing device according to the first aspect of the present disclosure, the first image included in the range of the measurement information is identified based on measurement information about a second image having a second region of interest corresponding to a first region of interest specified in the first image, and the first region of interest in the identified first image is measured. This allows accurate comparison of the measurement result of the first region of interest in the first image with the measurement result of the second region of interest in the second image.

[0011] In the image processing device of the second aspect, in the image processing device of the first aspect, one or more processors may identify, as the first image included in the range of the measurement information, the first image having the first image conditions that maximize the similarity index representing the similarity with the second image conditions of the second image.

[0012] According to this aspect, even if there is no first image having the same first image conditions as the second image conditions, it is possible to identify a first image that will enable accurate comparison of measurement results with the measurement results of the second image.

[0013] In the image processing device of the third aspect, in the image processing device of the second aspect, one or more processors may derive a similarity index using at least one of the reconstruction conditions, the slice thickness representing the thickness of a slice in a tomographic image, the spatial resolution, and the imaging protocol.

[0014] According to this aspect, a first image having a first image condition close to a second image condition can be identified based on at least one of the reconstruction condition, slice thickness, spatial resolution, and imaging protocol.

[0015] In an image processing device according to a fourth aspect, in an image processing device according to any one of the first to third aspects, one or more processors may apply, as measurement information, at least one of a second image in which measurement of the second region of interest is performed and the measurement results of the second region of interest.

[0016] According to this aspect, based on at least one of the second image and the measurement result of the second region of interest, it is possible to identify the first image that allows accurate comparison with the measurement result of the second region of interest.

[0017] In an image processing device according to a fifth aspect, in an image processing device according to any one of the first to fourth aspects, one or more processors may estimate an error in the measurement results of the first region of interest relative to the measurement results of the second region of interest based on the first image conditions and the second image conditions.

[0018] According to this aspect, when measuring a first region of interest in a first image having first image conditions close to second image conditions, it is possible to grasp errors in the measurement results caused by differences between the first image conditions and the second image conditions.

[0019] An image processing device according to a sixth aspect is the image processing device according to the fifth aspect, wherein the one or more processors may output an alert when the estimated error exceeds a specified range.

[0020] According to this aspect, it may be possible to consider redoing the measurement of the first region of interest in the first image and redoing the specific part of the first image.

[0021] In the image processing device according to the seventh aspect, in the image processing device according to the fifth or sixth aspect, one or more processors may cause a display device to display an error range defined according to the estimated error trend.

[0022] According to this aspect, the user can grasp the range of error in measuring the first region of interest in the first image.

[0023] An image processing device according to an eighth aspect is an image processing device according to any one of the first to seventh aspects, wherein one or more processors may set first display conditions to be applied to measuring a first region of interest in a first image in accordance with second display conditions to be applied to measuring a second region of interest in a second image.

[0024] According to this aspect, when measuring the first region of interest, the first region of interest and the second region of interest to be compared can be displayed in the same display aspect.

[0025] An image processing device according to a ninth aspect is the image processing device according to the eighth aspect, wherein the one or more processors may set at least one of brightness, window level, and window width as the first display condition.

[0026] According to this aspect, the first region of interest and the second region of interest can be displayed with at least one of the brightness, the window level, and the window width adjusted.

[0027] In an image processing device according to a tenth aspect, in an image processing device according to any one of the first to ninth aspects, one or more processors may, when receiving an input to select a first region of interest in a first image, display at least one of the first region of interest and the measurement results of the first region of interest on a display device.

[0028] In this aspect, an input for selecting a first region of interest in the first image in response to a user operation may be acquired.

[0029] An image processing device according to an eleventh aspect is an image processing device according to any one of the first to ninth aspects, wherein one or more processors, when receiving an input to select a first region of interest in a first image, may display at least one of the first region of interest and a measurement result of the first region of interest on a display device, and may also display measurement information on the display device.

[0030] According to this aspect, it is possible to contribute to improving convenience for the user.

[0031] In this aspect, an input for selecting a first region of interest in the first image in response to a user operation may be acquired.

[0032] An image processing device according to a 12th aspect is an image processing device according to the 11th aspect, wherein one or more processors may apply different display modes to the first region of interest, at least one of the measurement results of the first region of interest, and the measurement information.

[0033] According to this aspect, it is possible to easily distinguish between information belonging to the first image and information belonging to the second image.

[0034] In this aspect, an input for selecting a first region of interest in the first image in response to a user operation may be acquired.

[0035] In the image processing device of the 13th aspect, in the image processing device of the 11th aspect, when one or more processors receive input selecting a first region of interest in a first image, they may display at least one of the measurement results of the second image and the second region of interest on a display device as measurement information.

[0036] In this aspect, an input for selecting a first region of interest in the first image in response to a user operation may be acquired.

[0037] An image processing device according to a 14th aspect is an image processing device according to any one of the first to 13th aspects, wherein one or more processors receive, as the plurality of first images, a plurality of first medical images generated by photographing a subject, specify, as the first region of interest, an anatomical structure of the subject in the plurality of first medical images, and acquire, as the measurement information, measurement information obtained by measuring, for a plurality of second medical images generated by photographing the subject, an anatomical structure of the subject, which is a second region of interest corresponding to the first region of interest.

[0038] According to this aspect, it is possible to realize an accurate comparison between the measurement result of the first region of interest in the first medical image and the measurement result of the second region of interest in the second medical image.

[0039] An image processing device according to a 15th aspect is an image processing device according to the 14th aspect, wherein one or more processors may cause a display device to display measurement results of an anatomical structure as measurement results of a first region of interest in the first image.

[0040] According to this aspect, accurate comparison can be achieved between the measurement results of the first region of interest and the second region of interest to which the anatomical structure is applied.

[0041] A method for operating an image processing device according to a sixteenth aspect of the present disclosure is a method for operating an image processing device that includes one or more processors and one or more memories that store programs to be executed by the one or more processors, wherein the one or more processors receive a plurality of first images that are generated by photographing a subject, each of which is subject to a plurality of different first image conditions, specify a first region of interest in the plurality of first images, obtain measurement information for a plurality of second images that are generated by photographing the subject before photographing the first images, the second images having a second region of interest corresponding to the first region of interest, identify a first image from the plurality of first images that falls within the range of the measurement information, and measure the first region of interest in the identified first image.

[0042] According to the method for operating an image processing device according to the sixteenth aspect of the present disclosure, it is possible to obtain the same effects as those of the image processing device according to the first aspect of the present disclosure.

[0043] In the method for operating an image processing device according to the sixteenth aspect, it is possible to appropriately combine the same features as those specified in the second to fifteenth aspects. In this case, the components that perform the processes or functions specified in the image processing device can be understood as components of the method for operating an image processing device that perform the corresponding processes or functions.

[0044] A program according to a seventeenth aspect of the present disclosure is a program that causes a computer to perform the following functions: receive a plurality of first images generated by photographing a subject, each of which is subjected to a plurality of different first image conditions; specify a first region of interest in the plurality of first images; obtain measurement information for a plurality of second images generated by photographing a subject before photographing the first images, the second images having a second region of interest corresponding to the first region of interest; identify a first image from the plurality of first images that falls within the range of the measurement information; and measure the first region of interest in the identified first image.

[0045] According to the program according to the seventeenth aspect of the present disclosure, it is possible to obtain the same effects as those of the image processing device according to the first aspect of the present disclosure.

[0046] In the program according to the seventeenth aspect, it is possible to appropriately combine the same features as those specified in the second to fifteenth aspects. In this case, the components that perform the processes or functions specified in the image processing device can be understood as components of the program that perform the corresponding processes or functions. [Effects of the Invention]

[0047] According to the present invention, a first image included in the range of the measurement information is identified based on measurement information about a second image having a second region of interest corresponding to a first region of interest specified in the first image, and the first region of interest in the identified first image is measured, thereby enabling accurate comparison of the measurement results of the first region of interest in the first image and the measurement results of the second region of interest in the second image. [Brief explanation of the drawings]

[0048] [Figure 1] FIG. 1 is a diagram showing the overall configuration of a medical image processing system. [Figure 2] FIG. 2 is a schematic diagram of the image processing method according to the embodiment. [Figure 3] FIG. 3 is a schematic diagram showing the procedure of the image processing method according to the first embodiment. [Figure 4] FIG. 4 is a functional block diagram showing the electrical configuration of the image processing device according to the first embodiment. [Figure 5] FIG. 5 is a block diagram schematically illustrating an example of the hardware configuration of the image processing device according to the first embodiment. [Figure 6] FIG. 6 is a table showing the priorities assigned to the image conditions. [Figure 7] FIG. 7 is a table showing points assigned to image conditions. [Figure 8] FIG. 8 is a schematic diagram showing an example of the configuration of a user interface. [Figure 9] FIG. 9 is a schematic diagram showing the procedure of the image processing method according to the second embodiment. [Figure 10] FIG. 10 is a functional block diagram showing the electrical configuration of an image processing apparatus according to the second embodiment. [Figure 11] FIG. 11 is a block diagram schematically illustrating an example of the hardware configuration of an image processing device according to the second embodiment. [Figure 12] FIG. 12 is a schematic diagram showing the procedure of the image processing method according to the third embodiment. [Figure 13] 13 is a functional block diagram showing the electrical configuration of an image processing device according to the third embodiment. [Figure 14] FIG. 14 is a block diagram schematically showing an example of the hardware configuration of an image processing system according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0049] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings. In this specification, the same components are designated by the same reference numerals, and redundant explanations will be omitted where appropriate.

[0050] [Example of medical image processing system configuration] Figure 1 is a diagram showing the overall configuration of a medical image processing system. The medical image processing system 10 shown in the figure is a system that automatically performs image processing on medical images, capturing images of a subject under examination and supporting medical image diagnosis of the captured images. The subject under examination may also be referred to as a test subject or the like.

[0051] 1 includes medical image inspection equipment 12, a medical image database 14, an image processing device 16, a user terminal 20, and a cloud server 26. The medical image inspection equipment 12, the medical image database 14, the image processing device 16, and the user terminal 20 are installed in a medical institution such as a hospital, and are connected to each other via an intra-hospital network 22 so that data can be transmitted and received. A LAN (Local Area Network) can be used as the intra-hospital network 22. The intra-hospital network 22 may be wired or wireless.

[0052] The hospital network 22 is connected to the Internet 24 via a router (not shown). The hospital network 22 and the cloud server 26 are connected to each other via the Internet 24 so as to be able to freely send and receive data to and from each other.

[0053] The medical image inspection equipment 12 is an imaging device that captures an image of an area to be inspected of a subject and generates a medical image. Examples of the medical image inspection equipment 12 include an X-ray imaging device, a CT device, an MRI device, a PET device, an ultrasound device, and a CR device using a flat X-ray detector.

[0054] CT is an abbreviation for Computed Tomography, MRI is an abbreviation for Magnetic Resonance Imaging, PET is an abbreviation for Positron Emission Tomography, and CR is an abbreviation for Computed Radiography.

[0055] The medical image database 14 is a database that manages medical images captured using the medical image inspection equipment 12. A computer equipped with a large-capacity storage device can be used as the medical image database 14. Software that provides the functions of a database management system is installed in the computer.

[0056] The medical image database 14 may be configured as part of a picture archiving and communication system (PACS), which is an abbreviation for Picture Archiving and Communication Systems, that stores and manages medical images.

[0057] The DICOM standard can be applied to the format of medical images. DICOM tag information defined in the DICOM standard may be added to medical images. Note that the term "image" in this specification can include not only the meaning of the image itself, such as a photograph, but also the meaning of image data, which is a signal representing an image. Note that DICOM is an abbreviation for Digital Imaging and Communications in Medicine.

[0058] The image processing device 16 performs predetermined image processing on medical images generated by photographing the subject using the medical image inspection equipment 12 and medical images stored in the medical image database 14. The image processing device 16 is implemented by a computer. The computer may be a personal computer, a workstation, or a tablet terminal. The computer may also be a virtual machine.

[0059] The user terminal 20 is a terminal device used by a user such as a doctor, and may be, for example, a known image viewer for image interpretation. The user terminal 20 may be a personal computer, a workstation, or a tablet terminal.

[0060] The user terminal 20 includes an input device 20A and a display device 20B. The display device 20B displays medical images captured by the medical image inspection equipment 12 and the results of image processing. The display device 20B also displays the results of image processing performed by the image processing device 16 and the cloud server 26. A user can input instructions to the medical image processing system 10 using the input device 20A.

[0061] Cloud server 26 may be, for example, a server computer, a personal computer, or a workstation. Cloud server 26 performs image processing on medical images of subjects, including at least one of lesion extraction and disease name determination processing. Cloud server 26 is accessible from intra-hospital networks 22 of multiple hospitals via Internet 24.

[0062] [Overview of image processing method according to embodiment] Fig. 2 is a schematic diagram of an image processing method according to an embodiment. The image processing method according to the embodiment is implemented by the image processing device 16 shown in Fig. 1. When measuring a lesion in a current image, the image processing method according to the embodiment acquires measurement information from a previous examination of a lesion specified in the current image, and identifies a current image having image conditions that are the same as or similar to those of the past image used in the previous measurement from a current image group that includes multiple current images based on the acquired measurement information.

[0063] Specifically, a current image group CIG including a plurality of current images CI shown in Fig. 2 is acquired. In Fig. 2, current image CI1, current image CI2, and current image CI3 are shown as examples of the plurality of current images CI. The current image group CIG may be acquired from the medical image inspection equipment 12 shown in Fig. 1, or from the medical image database 14. Note that the image processing method described in the embodiment is an example of a method for operating an image processing device.

[0064] Figure 2 shows an example of a CT image, which is a two-dimensional slice image representing an axial cross section reconstructed from three-dimensional volume data, as a medical image. The medical images shown in Figure 3 and other figures are also two-dimensional slice images.

[0065] 2 also illustrates the reconstruction conditions and slice thicknesses as image conditions for multiple current images CI. The reconstruction conditions for current image CI1 are mediastinum conditions, and the slice thickness of current image CI1 is 5 mm. The reconstruction conditions for current image CI2 are lung field conditions, and the slice thickness of current image CI2 is 5 mm. The reconstruction conditions for current image CI3 are lung field conditions, and the slice thickness of current image CI3 is 1 mm.

[0066] Next, for a lesion specified in any current image CI, a past image PI used for lesion measurement during the previous examination is obtained. The past image PI is obtained from the medical image database 14 shown in Figure 1. The reconstruction conditions for the past image PI shown in Figure 2 are lung field conditions, and the slice thickness of the past image PI is 1 millimeter.

[0067] Next, a current image CI having the same image conditions as the previous image PI or image conditions close to those of the previous image PI is identified. In the example shown in Figure 2, a current image CI3 having the same image conditions as the previous image PI is identified.

[0068] The current image CI1 has different reconstruction conditions and slice thickness from the past image PI. The current image CI1 is a current image CI whose image conditions are inconsistent and deviate from those of the past image PI.

[0069] The current image CI2 has the same reconstruction conditions as the past image PI, but the slice thickness is different. The current image CI2 has some of the same image conditions and is a current image CI with image conditions that are closer to those of the past image PI.

[0070] For example, when the reconstruction conditions are mediastinal conditions, edges tend to be blurred and unclear, and lesions tend to be measured as small. On the other hand, when the reconstruction conditions are lung field conditions, edges tend to be sharp and clear, and lesions tend to be measured as large.

[0071] Furthermore, when the slice thickness is 5 mm, the partial volume effect tends to cause the lesion to be measured smaller than when the slice thickness is 1 mm. Therefore, unless lesion measurement is performed using a current image CI that has image conditions identical to or similar to those of the previous image PI, which is the subject of comparison of the measurement results, accurate comparison of the current lesion with the previous lesion becomes difficult. The image processing method according to the embodiment solves the above problem and enables favorable comparison of the measurement results of the lesion in the current image CI with the measurement results of the lesion in the previous image.

[0072] [Procedure of image processing method according to the first embodiment] Fig. 3 is a schematic diagram showing the procedure of the image processing method according to the first embodiment. In a current image group receiving step S10, the image processing device 16 shown in Fig. 1 receives a current image group CIG including a plurality of current images CI having different image conditions. Fig. 3 shows, as examples of the plurality of current images CI, current images CI11, CI12, CI13, and CI14, which are generated by performing a series of imaging operations on the same patient using a CT device, which is the medical imaging inspection equipment 12.

[0073] The image processing device 16 may receive a plurality of current images CI from a medical image inspection device 12 such as a CT device shown in FIG. 3, or may receive a plurality of current images CI from a medical image database 14.

[0074] Current image CI11 shown in FIG. 3 is an axial section image, the reconstruction conditions are mediastinal conditions, and the slice thickness is 5 mm. Current image CI12 is an axial section image, the reconstruction conditions are lung field conditions, and the slice thickness is 5 mm. Current image CI13 is an axial section image, the reconstruction conditions are lung field conditions, and the slice thickness is 1 mm. Current image CI14 is a coronal section image, the reconstruction conditions are mediastinal conditions, and the slice thickness is 3 mm.

[0075] In the lesion designation step S12, the image processing device 16 designates a lesion RI for any current image CI among the multiple current images CI received in the current image group receiving step S10. For example, any current image CI is displayed on the display device 20B of the user terminal 20, and the user can operate the input device 20A of the user terminal 20 to designate a lesion RI for any current image CI displayed on the display device 20B.

[0076] In the measurement information acquisition step S14, the image processing device 16 acquires measurement information obtained by measuring the lesion in the past image PI for the lesion RI specified in any current image CI. That is, the image processing device 16 extracts medical images with matching patient information and examination information from the medical images stored in the medical image database 14, and searches for past measurement results or past images used in past measurements as past measurement information for the lesion RI specified in any current image CI from the extracted medical images. The patient information includes patient identification information such as the patient's name. The examination information includes examination identification information such as an examination ID and information on the modality used in the examination. Note that ID is an abbreviation for identification.

[0077] 3 illustrates a past image PI11 and a measurement result PR of the past image PI11 as past measurement information. The past measurement information may be the past image PI11, the measurement result PR of the past image PI11, or the past image PI11 and the measurement result PR of the past image PI11.

[0078] In the current image specification step S16, a current image CI having image conditions identical to or similar to those of the past image PI11 acquired as the past measurement information is specified. That is, the image processing device 16 specifies a current image CI included in the range of the past measurement information from the current image CI11, the current image CI12, the current image CI13, and the current image CI14.

[0079] The reconstruction conditions for the past image PI11 are lung field conditions, and the slice thickness is 1 mm. The reconstruction conditions for the current image CI13 are lung field conditions, and the slice thickness is 1 mm, and the image conditions for the current image CI13 match the image conditions for the past image PI11. Therefore, in the current image identification step S16, the current image CI13, whose image conditions match those of the past image PI11 used in the past measurement, is identified and automatically selected.

[0080] 3 exemplifies the reconstruction conditions and slice thickness as image conditions. The image conditions may include pixel spacing, imaging protocol, etc. That is, the image conditions may include at least one of the reconstruction conditions, slice thickness, pixel spacing, and imaging protocol. Note that the slice thickness described in the embodiment is an example of a slice thickness representing the thickness of a slice in a tomographic image, and the pixel spacing described in the embodiment is an example of spatial resolution.

[0081] In the current image measurement step S18, the image processing device 16 measures the lesion RI for the current image CI13 identified in the current image identification step S16. The image processing device 16 may display the measurement results of the lesion RI on the display device 20B of the user terminal 20. Examples of measurement targets for the lesion RI include the major axis, minor axis, volume, average CT value, and average signal value of the lesion RI.

[0082] The current image CI described in the embodiments is an example of a first image, and is an example of a first medical image. The past image PI described in the embodiments is an example of a second image generated by capturing an image of a subject before capturing the first image, and is an example of a second image having a second region of interest corresponding to the first region of interest. The past image PI described in the embodiments is also an example of a second medical image.

[0083] Furthermore, the lesion RI specified in any current image CI described in the embodiments is an example of a first region of interest. The lesion in the past image PI described in the embodiments is an example of a second region of interest. The image conditions of the current image described in the embodiments are an example of a first image condition. Past measurement information described in the embodiments is an example of measurement information.

[0084] [Example of electrical configuration of image processing device according to first embodiment] Fig. 4 is a functional block diagram showing the electrical configuration of the image processing device according to the first embodiment. The image processing device 16 shown in the figure includes a current image group receiving unit 30. The current image group receiving unit 30 executes the current image group receiving step S10 shown in Fig. 3 to receive a current image group CIG including a plurality of current images.

[0085] The image processing device 16 includes a lesion designation unit 32 and an input information acquisition unit 34. The lesion designation unit 32 executes a lesion designation step S12 and designates a lesion RI using any current image CI included in the multiple current images CI. Examples of lesions include tumors, masses, and ulcers. The lesion designation unit 32 may designate anatomical structures such as bones, nerves, and blood vessels as regions of interest.

[0086] The input information acquisition unit 34 acquires input information for the image processing device 16. For example, the input information acquisition unit 34 may acquire information specifying a lesion RI by the user for any current image CI. The lesion designation unit 32 may designate a lesion RI for any current image CI based on the information specifying a lesion RI by the user for any current image CI acquired via the input information acquisition unit 34.

[0087] The image processing device 16 includes a measurement information acquisition unit 36. The measurement information acquisition unit 36 ​​executes a measurement information acquisition step S14, searches the medical image database 14, and acquires past measurement information of the lesion RI specified for the current image CI from the medical image database 14. The past measurement information may be a past image PI in which the lesion RI was measured, or may be a past measurement result PR of the lesion RI.

[0088] The image processing device 16 includes a current image specifying unit 38. In a current image specifying step S16, the current image specifying unit 38 specifies the image conditions of a past image PI corresponding to the past measurement information acquired using the measurement information acquiring unit 36, and specifies a current image CI having the same image conditions as the image conditions of the specified past image PI or image conditions close to the image conditions of the specified past image PI. The current image specifying unit 38 specifies a current image CI having the image conditions that satisfy the above from among the multiple current images CI acquired in the current image group receiving step S10.

[0089] The image processing device 16 includes a measurement unit 40 and a measurement condition setting unit 42. The measurement unit 40 applies the measurement conditions set using the measurement condition setting unit 42 to measure the lesion RI of the current image CI identified by the current image identification unit 38. The measurement of the lesion RI in the current image CI may be performed by extracting pixels included in the lesion RI, or may be performed based on pixels in an area specified by the user. The measurement conditions include measurement parameters for the lesion RI, such as the major axis, minor axis, and area.

[0090] The image processing device 16 includes a measurement result output unit 44. The measurement result output unit 44 outputs the measurement results of the lesion area RI in the current image CI measured using the measurement unit 40. An example of outputting the measurement results is displaying the measurement results on the display device 20B shown in Fig. 1. Another example of outputting the measurement results is printing the measurement results using a printing device.

[0091] [Example of hardware configuration of image processing device according to first embodiment] 5 is a block diagram schematically illustrating an example of the hardware configuration of an image processing device according to the first embodiment. The image processing device 16 includes one or more processors 52 and one or more memories 62. The image processing device 16 also includes a communication interface 56 and an input / output interface 58.

[0092] The processor 52 executes various programs stored in a memory 62 of the computer-readable medium 54 to realize various functions of the image processing device 16. The processor 52 includes a CPU (Central Processing Unit). The processor 52 may also include a GPU (Graphics Processing Unit). The processor 52 is connected to the computer-readable medium 54, a communication interface 56, and an input / output interface 58 via a bus 60.

[0093] The computer-readable medium 54 includes a memory 62 serving as a main storage device and a storage 64 serving as an auxiliary storage device. The computer-readable medium 54 may be a semiconductor memory, a hard disk drive, a solid-state drive, or the like. The computer-readable medium 54 may be any combination of multiple devices.

[0094] A hard disk drive may be referred to as an HDD, which is an abbreviation of the English term Hard Disk Drive, and a solid state drive may be referred to as an SSD, which is an abbreviation of the English term Solid State Drive.

[0095] The memory 62 of the computer readable medium 54 stores a current image set receiving program 70 , a lesion designation program 72 , a measurement information acquisition program 74 , a current image identification program 76 , and a measurement program 78 .

[0096] The current image group receiving program 70 is applied to the current image group receiving unit 30 shown in Figure 4 and realizes the current image group receiving function. The lesion designation program 72 is applied to the lesion designation unit 32 and realizes the lesion designation function. The measurement information acquisition program 74 is applied to the measurement information acquisition unit 36 ​​and realizes the measurement information acquisition function. The current image identification program 76 is applied to the current image identification unit 38 and realizes the current image identification. The measurement program 78 is applied to the measurement unit 40 and realizes the measurement function.

[0097] Various programs stored in the computer-readable medium 54 include one or more instructions. Various data, various parameters, etc. are stored in the computer-readable medium 54. The term "program" is synonymous with the term "software."

[0098] The hardware structure of the processor 52 is various processors as follows: The various processors include a CPU (Central Processing Unit), which is a general-purpose processor that executes software (programs) and functions as various functional units, a GPU (Graphics Processing Unit), which is a processor specialized for image processing, a PLD (Programmable Logic Device), which is a processor whose circuit configuration can be changed after manufacturing such as an FPGA (Field Programmable Gate Array), and a dedicated electric circuit, which is a processor having a circuit configuration designed specifically for executing specific processing such as an ASIC (Application Specific Integrated Circuit).

[0099] A single processing unit may be configured with one of these various processors, or may be configured with two or more processors of the same or different types (e.g., multiple FPGAs, a combination of a CPU and an FPGA, or a combination of a CPU and a GPU). Also, multiple functional units may be configured with a single processor. Examples of multiple functional units configured with a single processor include, first, a configuration in which a single processor is configured with a combination of one or more CPUs and software, as typified by a client or server computer, and this processor operates as multiple functional units. Second, a configuration in which a processor is used to realize the functions of an entire system including multiple functional units on a single IC (Integrated Circuit) chip, as typified by an SoC (System On Chip). In this way, the various functional units are configured with one or more of the above-mentioned various processors as a hardware structure.

[0100] Furthermore, the hardware structure of these various processors is, more specifically, an electric circuit made up of a combination of circuit elements such as semiconductor elements.

[0101] The memory 62 stores instructions to be executed by the processor 52. The memory 62 includes a RAM (Random Access Memory) and a ROM (Read Only Memory), not shown. The processor 52 uses the RAM as a working area and executes software using various programs and parameters, including image processing programs, such as the current image group receiving program 70, stored in the ROM, and also executes various processes of the image processing device 16 by using the parameters stored in the ROM, etc.

[0102] The user terminal 20 shown in Fig. 1 may realize various functions of the image processing device 16. That is, the user terminal 20 may include various processing units such as the current image group receiving unit 30 shown in Fig. 4, and may execute various steps such as the current image group receiving step S10 shown in Fig. 3. Furthermore, the cloud server 26 shown in Fig. 1 may realize various functions of the image processing device 16.

[0103] [Specific example of a specific current image for measuring the lesion] FIG. 6 is a table showing the priorities assigned to image conditions. The figure shows examples of image conditions, such as reconstruction conditions, slice thickness, pixel spacing, and imaging protocol. The imaging protocol may include a distinction between contrast and non-contrast imaging. The imaging protocol may also include a dose.

[0104] 3, the number of matches of image conditions is calculated for all current images CI included in the current image group CIG. The current image CI with the largest number of matches is identified as the current image CI with similar image conditions. If there are multiple current images CI with the same number of matches, the current image CI that matches the image condition with the highest priority is identified as the current image CI with similar image conditions.

[0105] FIG. 7 is a table showing points assigned to image conditions. Points may be defined for each image condition. In the current image identification step S16, the sum of points of matching image conditions is calculated for all current images CI included in the current image group CIG. The current image CI with the maximum points is identified as the current image CI with the closest image conditions. If there are multiple current image CIs with the same sum of points, the current image CI that matches the image condition with the highest priority is identified as the current image CI with the closest image conditions.

[0106] The sum of points described in the embodiment is an example of a similarity index representing the similarity with the second image condition. The image condition of the past image PI described in the embodiment is an example of the second image condition.

[0107] [User interface configuration example] Fig. 8 is a schematic diagram showing an example of the configuration of a user interface. When a user performs a click operation on a lesion area RI in a current image CI displayed on the display device 20B shown in Fig. 2, the screen displaying the current image CI may transition to a screen displaying a pop-up display of a past image PI and measurement results in the past image PI, and a pop-up display of measurement results in the current image CI for the current image CI.

[0108] 8, the screen after the transition may display an enlarged view of the lesion RI, and text information representing the measurement results may be superimposed on a position adjacent to the lesion RI but not overlapping the lesion RI. Furthermore, the lesion RI in the past image PI may be distinguished from the lesion RI in the current image CI using color. The measurement results in the past image PI may be distinguished from the measurement results in the current image CI using color.

[0109] The same color may be applied to the lesion RI in the past image PI and the measurement results of the past image PI. The same color may be applied to the lesion RI in the current image CI and the measurement results of the current image CI. The past image PI may be displayed with text information representing the past image PI and text information representing the examination date superimposed thereon. The current image CI may be displayed with text information representing the current image CI and text information representing the examination date superimposed thereon. In other words, the past image PI and information related to the past image PI and the current image CI and information related to the current image CI are displayed in different display modes, allowing them to be visually distinguished.

[0110] [Effects of the first embodiment] The image processing method and image processing device according to the first embodiment can achieve the following advantageous effects.

[0111] [1] When measuring a lesion RI specified in a current image CI, past measurement information of the lesion RI to be measured is acquired, a current image CI within the range of the past measurement information is identified, and the lesion RI in the identified current image CI is measured. This allows for an accurate comparison of the lesion RI in the current image CI with the lesion RI in the past image PI.

[0112] [2] The current image CI within the range of the past measurement information includes a current image CI having the same image conditions as the past image PI on which the past measurement was performed, and a current image CI having image conditions close to the image conditions of the past image PI on which the past measurement was performed. This makes it possible to identify the current image CI on which the measurement of the lesion RI is performed, even if there is no current image CI having the same image conditions as the past image PI on which the past measurement was performed.

[0113] [3] The image conditions similar to those of the previous image PI are determined based on the similarity of the reconstruction conditions, slice thickness, pixel spacing, and imaging protocol. This allows the current image CI suitable for measuring the lesion RI to be identified.

[0114] [4] The proximity of the image conditions is determined based on a predetermined priority, thereby enabling accurate determination of the proximity of the image conditions.

[0115] [Procedure of image processing method according to the second embodiment] Fig. 9 is a schematic diagram showing the steps of an image processing method according to the second embodiment. The image processing method according to the second embodiment includes a current image group receiving step S11 instead of the current image group receiving step S10 shown in Fig. 3. Furthermore, the image processing method according to the second embodiment adds an estimated error deriving step S20 and an estimated error output step S22 to the image processing method according to the first embodiment.

[0116] 9 receives a current image group CIG including current images CI21, CI22, and CI24. In the lesion designation step S12, a lesion RI is designated for any current image CI included in the current image group CIG, similar to the lesion designation step S12 shown in FIG.

[0117] In the measurement information acquisition step S14, past measurement information of the specified lesion RI is acquired, similar to the measurement information acquisition step S14 shown in Fig. 3. Fig. 9 shows an example in which a past image PI21 and past measurement results are acquired as past measurement information.

[0118] In the current image specification step S16, since there is no current image CI having the same image conditions as the previous image PI21, a current image CI22 having the same image conditions close to the image conditions of the previous image PI21 is specified.

[0119] Here, the past image PI21 and the current image CI22 have the same reconstruction conditions but different slice thicknesses. The measurement result of the lesion area RI in the current image CI22, which has different image conditions from the past image PI21, may have an error compared to the measurement result of the lesion area RI in the past image PI21.

[0120] In the estimation error derivation step S20, an estimation error between the measurement result of the lesion area RI in the past image PI21 and the measurement result of the lesion area RI in the current image CI22 is derived. In the estimation error derivation step S20, an estimation error database is used in which the estimation error for each combination of image conditions is stored.

[0121] The estimated error database stores the errors of the measurement results obtained by performing measurements on the same lesion RI under different image conditions, in association with the combination of image conditions. An estimated error database may be created for each type of lesion. The estimated error database is illustrated in FIG. 10 using the reference numeral 82.

[0122] In the estimated error output step S22, the estimated error derived in the estimated error derivation step S20 is output. Fig. 9 shows an example of the output of the estimated error, in which text information is displayed on a display device. The figure shows an example in which the range of the estimated error is displayed as minus 3 millimeters to plus 1 millimeter.

[0123] 9, the slice thickness of the past image PI21 is 1 mm, while the slice thickness of the current image CI22 is 5 mm, and the current image CI22 is likely to be measured as smaller than the past image PI21. Therefore, the range of estimation error is adjusted so that the negative numerical range is larger than the positive numerical range.

[0124] An alert output step may be executed to output an alert when the estimation error exceeds a specified range. The alert may be output in the form of sound, voice, or display of text information. The alert may be output in a combination of multiple output forms, such as a combination of voice and text information. The display device described in the embodiment is an example of a display device.

[0125] [Example of electrical configuration of image processing device according to second embodiment] Fig. 10 is a functional block diagram showing the electrical configuration of an image processing device according to the second embodiment. The image processing device 16A shown in Fig. 10 additionally includes an estimated error derivation unit 80 and an estimated error output unit 84 compared to the image processing device 16 shown in Fig. 4. An alert output unit 86 may also be added to the image processing device 16A.

[0126] 9, the estimated error derivation unit 80 derives an estimated error of measurement of the lesion RI in the current image CI to which different image conditions than those of the past image PI are applied, by referring to the estimated error database 82. The estimated error database 82 may be provided in the image processing device 16A, or may be an external device to the image processing device 16A.

[0127] The estimated error output unit 84 executes the estimated error output step S22 and outputs the estimated error derived by the estimated error derivation unit 80. The estimated error output unit 84 may cause a display device to display the estimated error.

[0128] The alert output unit 86 outputs an alert when the estimated error derived by the estimated error derivation unit 80 exceeds a specified range. The alert output unit 86 may be provided with an alert output condition setting unit that sets an alert output condition.

[0129] [Example of hardware configuration of image processing device according to second embodiment] Fig. 11 is a block diagram schematically showing an example of the hardware configuration of an image processing device according to the second embodiment. The image processing device 16A shown in Fig. 11 includes a computer-readable medium 54A instead of the computer-readable medium 54 of the image processing device 16 shown in Fig. 5. The computer-readable medium 54A includes a memory 62A instead of the memory 62 shown in Fig. 5.

[0130] 5, various programs such as the current image group receiving program 70, and an estimated error derivation program 90 is also stored in the memory 62A. An alert output program 92 may also be added to the memory 62A.

[0131] The estimated error derivation program 90 is applied to the estimated error derivation unit 80 shown in Fig. 9 to realize the estimated error derivation function. The alert output program 92 is applied to the alert output unit 86 to realize the alert output function.

[0132] [Effects of the second embodiment] The image processing method and image processing device according to the second embodiment can achieve the following advantageous effects.

[0133] [1] When a current image CI is identified that is not identical to the previous image PI in image conditions but is similar to the previous image PI in image conditions, an estimated error for the measurement result of the current image CI is derived and output, thereby realizing an accurate comparison of the measurement results of the current image CI and the previous image PI.

[0134] [2] The estimation error is derived using an estimation error database in which the differences in the measurement results obtained by applying different image conditions and measuring the lesion area are stored in association with the combinations of image conditions, thereby enabling the derivation of a highly reliable estimation error.

[0135] [3] If the estimation error exceeds a specified range, an alert indicating this is output, allowing the user to understand when it is difficult to accurately compare the measurement results between the current image CI and the past image PI.

[0136] [Procedure of image processing method according to the third embodiment] 12 is a schematic diagram showing the procedure of the image processing method according to the third embodiment. The image processing method according to the third embodiment adds a display condition adjusting step S17 to the image processing method according to the first embodiment.

[0137] The display condition adjusting step S17 is executed after the current image specifying step S16, and the display conditions of the current image CI13 specified in the current image specifying step S16 are adjusted to match the display conditions of the past image PI11.

[0138] In the display condition adjustment step S17, the display conditions of the current image CI13 are adjusted using the display conditions of the past image PI acquired when acquiring the past measurement information in the measurement information acquisition step S14.

[0139] In the example shown in FIG. 12, the brightness of the past image PI11 is darker than the current image CI13 identified in the current image identification step S16, and a current image CI13A whose brightness has been adjusted to match the past image PI11 is displayed.

[0140] In the current image measurement step S18, measurement of the lesion RI is performed using the current image CI13A whose brightness has been adjusted to match the past image PI11. As the display conditions to be adjusted, at least one of a window level indicating a reference CT value to be displayed on the image and a window width indicating a range of CT values ​​to be displayed on the image may be applied.

[0141] The display conditions of the current image CI13 described in the embodiment are an example of a first display condition, and the display conditions of the past image PI11 described in the embodiment are an example of a second display condition.

[0142] [Example of electrical configuration of image processing device according to third embodiment] 13 is a functional block diagram showing the electrical configuration of an image processing device according to the third embodiment. The image processing device 16B shown in the drawing is obtained by adding a display adjustment unit 39 to the image processing device 16 shown in FIG.

[0143] The display adjustment unit 39 acquires the display conditions of the past image PI11 shown in Fig. 12 from the medical image database 14. The display adjustment unit 39 adjusts the display conditions of the current image CI13 identified using the current image identification unit 38, and displays the current image CI13A, for which the display conditions of the current image CI13 have been adjusted, on the display device 41. The display device 20B of the user terminal 20 shown in Fig. 1 can be used as the display device 41 shown in Fig. 13.

[0144] [Example of hardware configuration of image processing device according to the third embodiment] Fig. 14 is a block diagram showing an example of the hardware configuration of an image processing system according to the third embodiment. The image processing device 16B shown in Fig. 14 includes a computer-readable medium 54B instead of the computer-readable medium 54 of the image processing device 16 shown in Fig. 5. The computer-readable medium 54B includes a memory 62B instead of the memory 62 shown in Fig. 5.

[0145] The memory 62B stores various programs such as the current image group receiving program 70 stored in the memory 62 shown in Fig. 5, and also stores a display adjustment program 77. The display adjustment program 77 is applied to the display adjustment unit 39 shown in Fig. 13 to realize the display adjustment function.

[0146] [Effects of the third embodiment] The image processing method and image processing device according to the third embodiment can achieve the following advantageous effects.

[0147] [1] The display conditions of the current image CI specified as the measurement target of the lesion RI are adjusted to match the display conditions of the past image PI. This allows the current image CI of the measurement target of the lesion RI and the past image PI in which the lesion was measured to be displayed with a similar appearance, thereby satisfying the need to use the same current image CI as the past image PI when measuring the lesion RI.

[0148] [2] When past measurement information is acquired from the medical image database 14, the display conditions of the past images PI corresponding to the past measurement information are acquired. This makes it possible to use highly reliable display conditions of the past images PI.

[0149] The technical scope of the present invention is not limited to the scope described in the above embodiments. The configurations and the like in each embodiment can be appropriately combined with each other within the scope that does not deviate from the spirit of the present invention. [Explanation of symbols]

[0150] 10 Medical image processing system 12 Medical imaging equipment 14 Medical Image Database 16 Image processing device 16A Image processing device 16B Image processing device 20 User terminal 20A input device 20B Display device 22 Hospital Network 24 Internet 26 Cloud Server 30 Current image group receiving unit 32 Lesion Designation Department 34 Input information acquisition unit 36 Measurement information acquisition unit 38 Current image identification section 39 Display adjustment section 40 Measurement section 41 Display device 42 Measurement condition setting section 44 Measurement result output section 52 processors 54 Computer-readable medium 54A Computer-readable medium 54B Computer-readable medium 56 Communication Interface 58 Input / Output Interface 60 Bus 62 memory 62A Memory 62B memory 64 Storage 70 Current image collection receiving program 72 Lesion Designation Program 74 Measurement information acquisition program 76 Current Image Identification Program 77 Display Adjustment Program 78 Measurement Program 80 Estimation error derivation part 82 Estimation Error Database 84 Estimation error output section 86 Alert output section 90 Estimation error derivation program 92 Alert Output Program CI current image CI1 current image CI2 current image CI3 current image CI11 current image CI12 current image CI13 current image CI13A current image CI14 current image CI21 current image CI22 current image CI24 current image CIG current images PI Past Images PI11 Past Images PR measurement results RI lesion S10 to S22: Each step of the image processing method

Claims

1. one or more processors; one or more memories storing instructions for execution by the one or more processors; Equipped with The one or more processors: receiving a plurality of first images generated by photographing a subject, the plurality of first images being applied with a plurality of first image conditions different from one another; Specifying a first region of interest in the plurality of first images; acquiring measurement information for a plurality of second images generated by photographing the object before photographing the first image, the second images having a second region of interest corresponding to the first region of interest; Identifying a first image included in the range of the measurement information from the plurality of first images; An image processing device that measures the first region of interest in the identified first image.

2. The image processing device according to claim 1 , wherein the one or more processors identify, as the first image included in the range of the measurement information, the first image having the first image condition for which a similarity index representing similarity to the second image condition of the second image is maximized.

3. The image processing apparatus according to claim 2 , wherein the one or more processors derive the similarity index using at least one of a reconstruction condition, a slice thickness indicating a thickness of a slice in a tomographic image, a spatial resolution, and an imaging protocol.

4. The one or more processors: The image processing apparatus according to claim 1 , wherein at least one of the second image in which the second region of interest is measured and a measurement result of the second region of interest is applied as the measurement information.

5. The image processing apparatus according to claim 1 , wherein the one or more processors estimate an error in the measurement result of the first region of interest relative to the measurement result of the second region of interest based on the first image condition and the second image condition.

6. The image processing device according to claim 5 , wherein the one or more processors output an alert when the estimated error exceeds a specified range.

7. The image processing device according to claim 5 , wherein the one or more processors cause a display device to display the error range defined according to the estimated error tendency.

8. 5. An image processing device as claimed in any one of claims 1 to 4, wherein the one or more processors set a first display condition to be applied to the measurement of the first region of interest in the first image in accordance with a second display condition to be applied to the measurement of the second region of interest in the second image.

9. The image processing device according to claim 8 , wherein the one or more processors set at least one of brightness, a window level, and a window width as the first display condition.

10. 5. The image processing device according to claim 1, wherein when the one or more processors receive an input for selecting the first region of interest in the first image, the one or more processors display at least one of the first region of interest and a measurement result of the first region of interest on a display device.

11. An image processing device as described in any one of claims 1 to 4, wherein the one or more processors, when receiving an input selecting the first region of interest in the first image, display at least one of the first region of interest and a measurement result of the first region of interest on a display device, and display the measurement information on the display device.

12. The image processing device according to claim 11 , wherein the one or more processors apply different display modes to at least one of the first region of interest and the measurement result of the first region of interest, and the measurement information.

13. The image processing device described in claim 11, wherein the one or more processors, when receiving an input selecting the first region of interest in the first image, display at least one of the measurement results of the second image and the second region of interest on a display device as the measurement information.

14. The one or more processors: receiving a plurality of first medical images generated by photographing a subject as the plurality of first images; designating an anatomical structure of the subject in the plurality of first medical images as the first region of interest; The image processing device according to claim 1 , wherein the measurement information is obtained by measuring an anatomical structure of the subject, which is a second region of interest corresponding to the first region of interest, for a plurality of second medical images generated by photographing the subject.

15. The image processing apparatus of claim 14 , wherein the one or more processors cause a display device to display the measurement of the anatomical structure as the measurement of the first region of interest in the first image.

16. An image processing method comprising one or more processors and one or more memories in which a program to be executed by the one or more processors is stored, the method comprising: The one or more processors receiving a plurality of first images generated by photographing a subject, the plurality of first images being applied with a plurality of first image conditions different from one another; Specifying a first region of interest in the plurality of first images; acquiring measurement information for a plurality of second images generated by photographing the object before photographing the first image, the second images having a second region of interest corresponding to the first region of interest; Identifying a first image included in the range of the measurement information from the plurality of first images; A method of operating an image processing apparatus to measure the identified first region of interest in the first image.

17. On the computer, A function of receiving a plurality of first images generated by photographing a subject, the plurality of first images being applied with a plurality of first image conditions different from each other; specifying a first region of interest in the plurality of first images; a function of acquiring measurement information for a plurality of second images generated by photographing the subject before photographing the first image, the second images having a second region of interest corresponding to the first region of interest; A function of identifying the first image included in the range of the measurement information from the plurality of first images; and A program that realizes a function of measuring the first region of interest in the identified first image.

Images