Computer program, information processing device, and information processing method

Abstract

To provide a computer program, an information processing device and an information processing method which can adjust luminance only with a function image.SOLUTION: A computer program causes a computer to execute processing of: acquiring a function image imaged by detecting the radiation emitted from a radioactive pharmaceutical; receiving selection of the radioactive pharmaceutical used in the acquired function image; and adjusting the luminance of the function image according to the selected radioactive pharmaceutical to display the image.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a computer program, an information processing device, and an information processing method. [Background technology]

[0002] In recent years, the aging of the population has led to an increase in the number of dementia patients and those at risk of developing dementia (mild cognitive impairment). The three major diseases that cause dementia are Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and vascular dementia. Other diseases, such as multiple sclerosis (MS), also cause various neurological symptoms, including dementia. While the cause of Alzheimer's disease remains unknown, specific lesions are observed in the brain as the disease progresses. For example, the deposition of senile plaques caused by amyloid beta is known to occur outside of nerve cells. Amyloid beta in the brain can be visualized by injecting a drug that binds to amyloid beta in brain tissue into the subject, and PET (Positron Emission Tomography) images are used to display the concentration distribution of the drug on cross-sections of the brain.

[0003] To visually evaluate the distribution of amyloid beta in the brain by interpreting PET images, it is necessary to manually adjust the brightness of a predetermined reference region so that the signal value becomes the reference point of the color scale of the entire image. To identify the reference region, a technique such as that described in Patent Document 1 can be used, in which an MRI image of the entire head is segmented using a mask image to obtain an image of the part corresponding to the brain. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-247534 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in clinical settings, structural images such as MRI images are sometimes unavailable, and interpretation is often performed using only functional images such as PET images, which can make it difficult to adjust the brightness of functional images.

[0006] The present invention has been made in view of the above circumstances, and has an object to provide a computer program, an information processing device, and an information processing method that are capable of adjusting brightness using only functional images. [Means for solving the problem]

[0007] The present application includes a plurality of means for solving the above-mentioned problems. As one example, a computer program causes a computer to execute the following process: obtain a functional image by detecting radiation emitted from a radioactive drug; accept a selection of a radioactive drug used in the obtained functional image; and adjust and display the brightness of the functional image according to the selected radioactive drug. [Effects of the Invention]

[0008] According to the present invention, it is possible to adjust the brightness using only the functional image. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 2] FIG. 1 illustrates an example of a configuration of an information processing device. [Figure 3] FIG. 10 is a diagram illustrating an example of a method for generating a learning model. [Figure 4] FIG. 10 is a diagram illustrating an example of brightness adjustment. [Figure 5] FIG. 10 is a diagram showing an example of a display screen before brightness adjustment. [Figure 6] FIG. 10 is a diagram showing an example of a display screen after brightness adjustment. [Figure 7] FIG. 10 is a diagram illustrating an example of a processing procedure performed by an information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0010] An embodiment of the present invention will now be described with reference to the drawings. FIG. 1 is a diagram showing an example of the configuration of an information processing system according to this embodiment. The information processing system includes an information processing device 50. A client device 40 is connected to the information processing device 50 via a communication network 1. The client device 40 may be configured, for example, as a personal computer, and has an image viewer or the like installed thereon. An image server 30 is connected to the client device 40. The image server 30 receives medical images from a PET (Positron Emission Tomography) device 10 and stores the images in a database. The client device 40 acquires medical images from the image server 30 and transmits the acquired medical images to the information processing device 50. The information processing device 50 can perform predetermined processing on the medical images and transmit the processing results to the client device 40. The client device 40 can display the processing results. Details of the predetermined processing will be described later.

[0011] The PET device 10 can obtain PET images by intravenously injecting a positron-emitting diagnostic agent and detecting gamma rays emitted when positrons emitted from cells that have taken up the diagnostic agent are annihilated. The PET device 10 includes not only dedicated PET devices but also devices incorporating an X-ray CT (Computed Tomography) device or a SPECT (Single Photon Emission CT) device. That is, PET images, PET-CT images, and SPECT images can be obtained from the PET device 10. The PET device 10 is installed in, for example, a medical institution such as a hospital. Various images obtained by the PET device 10 are stored in an image server 30. PET images and SPECT images are also referred to as functional images.

[0012] PET and SPECT images are generated by administering a radioactive drug to a subject via intravenous injection or other means and capturing radiation emitted by the drug within the body. Drug-based imaging allows physicians to understand not only the morphology of various parts of the body, but also the distribution of the administered drug within the body and the accumulation of substances in the body that react with the drug, thereby contributing to improved accuracy in disease diagnosis. For example, PET images can be captured using Pittsburgh Compound B as a PET radioactive drug (tracer). Measurement of the level of amyloid beta protein accumulation in the brain based on the captured PET images can be useful for differential diagnosis or early diagnosis of Alzheimer's disease. Furthermore, SPECT images can also be used to examine various areas, such as cerebral blood flow tests for cerebrovascular disorders and dementia, depending on the type of drug.

[0013] In this specification, medical images include PET images, PET-CT images, SPECT images, CT images, MRI (Magnetic Resonance Imaging) images, etc. Furthermore, medical images include not only medical images related to the brain, but also medical images related to parts other than the brain.

[0014] The image server 30 records medical images (brain function images and brain structure images) for each patient. For example, for each patient, the date on which the medical image was taken, the imaging conditions, whether or not medication was administered at the time of imaging or the number of times medication was administered, the name and amount of the therapeutic drug administered, etc. are recorded in association with the medical image.

[0015] 2 is a diagram showing an example of the configuration of an information processing device 50. The information processing device 50 can be configured with a computer, and includes a control unit 51 that controls the entire information processing device 50, a communication unit 52, a memory 53, a preprocessing unit 54, a display control unit 55, a brightness adjustment unit 56, and a storage unit 57. The storage unit 57 stores a computer program 58 and a learning model 59.

[0016] The control unit 51 can be configured with a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit), etc. The control unit 51 can execute processing defined by a computer program 58. In other words, the processing by the control unit 51 is also processing by the computer program 58.

[0017] The communication unit 52 includes, for example, a communication module, and has a function of communicating with the client device 40 via the communication network 1. The communication unit 52 can acquire (receive) medical images (for example, functional images of a patient) from the client device 40.

[0018] The memory 53 can be configured with semiconductor memory such as SRAM (Static Random Access Memory), DRAM (Dynamic Random Access Memory), flash memory, etc. A computer program 58 can be loaded into the memory 53, and the control unit 51 can execute the computer program 58.

[0019] The storage unit 57 can be configured with, for example, a hard disk or semiconductor memory, and may store necessary information in addition to the computer program 58 and learning model 59.

[0020] The preprocessing unit 54 aligns the functional image (e.g., PET image or SPECT image) acquired via the communication unit 52 with the template image. Specifically, the preprocessing unit 54 performs rigid body transformation (transformation that allows only translation and rotation of coordinates) using a standard brain image (template image) prepared in advance, and performs processing to align the tilt and position of the brain in the functional image. This can improve the accuracy of the segmentation processing by the learning model 59. In addition, the tilt of the functional image can be automatically adjusted in accordance with interpretation guidelines.

[0021] The control unit 51 inputs the functional images that have been aligned by the preprocessing unit 54 into the learning model 59.

[0022] The learning model 59 is generated to output a specific reference region when a functional image is input. The reference region is a region on the functional image used to adjust the brightness of the entire functional image, and includes, for example, the cerebellum, pons, and the whole brain (a region excluding parts other than brain tissue, such as salivary glands and bones, in the PET image). Specific reference regions are defined in interpretation handbooks and guidelines according to the type of radiopharmaceutical used in the PET examination. The control unit 51 can input a functional image into the learning model 59 to obtain a specific reference region on the functional image. Separate learning models 59 may be used for each specific reference region. For example, when a functional image is input, a learning model 59 that outputs the cerebellum region and a learning model 59 that outputs the pons region may be separate models. The learning model 59 may use semantic segmentation such as U-Net or SegNet, or may use instance segmentation such as Mask R-CNN, DeepMask, or FCIS.

[0023] Figure 3 is a diagram showing an example of a method for generating the learning model 59. A required number of mask images with identified reference regions are prepared in advance as training data for generating the learning model 59. The mask images can be generated by performing segmentation processing on structural images such as MRI images.

[0024] The control unit 51 acquires training data including functional images as learning input data and mask images as teacher data. The training data may be collected and stored in, for example, the image server 30 or another data server (not shown) and acquired from the server. When a functional image is input based on the training data, the control unit 51 generates a learning model 59 so as to output a reference region identified by a mask image included in the training data. Specifically, the parameters of the learning model 59 may be adjusted so that the value of a loss function based on the output data (reference region) output by the learning model 59 and the teacher data (reference region on the mask image) is minimized.

[0025] The learning model 59 may be generated by the information processing device 50, or may be generated by a device separate from the information processing device 50. By using the learning model 59, it is possible to perform segmentation of the reference region using only functional images, even when structural images are not available.

[0026] The brightness adjustment unit 56 adjusts the brightness of the functional image based on the signal value of a specific reference region on the functional image.

[0027] The display control unit 55 can display the functional image, the brightness of which has been adjusted by the brightness adjustment unit 56, on the display screen of the client device 40. The display control unit 55 can perform display control to display required information on the display screen of the client device 40.

[0028] FIG. 4 is a diagram showing an example of brightness adjustment. In functional images such as PET images, a color scale is defined for each radiopharmaceutical to achieve optimal imaging for visually interpreting the accumulation state of amyloid beta. Examples of color scales include the Sokoloff scale, rainbow scale, and spectral scale. The color scale may also be a gray scale. As shown in FIG. 4, information indicating the correspondence between drugs (radiopharmaceuticals) and color scales can be stored, for example, in memory unit 57. In the example of FIG. 4, drugs are represented by symbols D1, D2, D3, ..., and the corresponding color scales are represented by CS1, CS2, CS3, ....

[0029] Before adjusting the brightness, the control unit 51 receives a drug selection from the client device 40. The drug selection is for identifying the drug used in the functional image. In the example of FIG. 4, drug D2 is selected, and color scale CS2 is used. The brightness adjustment unit 56 calculates the statistical value of the signal value of the reference region output by the learning model 59. The statistical value of the signal value may be any of the mean, median, and percentile value. The brightness adjustment unit 56 adjusts the brightness of the functional image based on a predetermined value of the color scale CS2 and the statistical value of the signal value of the reference region. Specifically, for example, the minimum brightness value can be set to 0, and the maximum brightness value can be calculated so that the statistical value of the signal value becomes the predetermined value of the color scale CS2. If the predetermined value is 0.9 (90%) of the maximum value, the maximum brightness value can be calculated as follows: Maximum value = (Statistical value of the signal value of the reference region) / 0.9. Note that the method for calculating the maximum brightness is not limited to this, and it may be calculated according to the requirements of the guidelines corresponding to the drug used.

[0030] Next, a display example of the display screen of the client device 40 will be described.

[0031] Fig. 5 is a diagram showing an example of the display screen 100 before brightness adjustment. As shown in Fig. 5, the display screen displays attribute information about the patient, such as the patient ID (which may include the name), date of birth, sex, the date the medical image was taken (the date when medication was administered), medication, and medication history. The patient ID may be selectable from among multiple patients. Furthermore, if there are multiple imaging dates, the imaging date may be selectable.

[0032] In the image area 101, desired slice images from multiple tomographic images (e.g., brain functional images such as PET images) can be displayed in the form of sagittal, coronal, and axial slices. The functional images displayed in the image area 101 are before brightness adjustment and use the minimum and maximum values ​​of the entire image, which are the default values ​​of many image viewers. This may result in the degree of accumulation of amyloid beta in the brain becoming unclear.

[0033] PET images include, for example, distribution information (e.g., SUVR values ​​for each voxel) visualizing the distribution of amyloid beta in the brain. SUVR (Standardized Uptake Value Ratio) can be calculated by summing the SUV (Standardized Uptake Value: amyloid beta protein accumulation) of four regions of the cerebral gray matter (prefrontal cortex, anterior and posterior cingulate cortex, parietal lobe, and lateral temporal lobe) and dividing it by the SUV of a specific reference region (e.g., cerebellum). The accumulation level (SUV) of amyloid beta can be calculated, for example, by counting the number of voxels whose brightness values ​​for each voxel constituting a given region are equal to or greater than a predetermined threshold. Accumulation (e.g., 0%, etc.) can be calculated from the ratio of the count value to the total number of voxels in the given region.

[0034] A user such as a doctor can select the drug used in the functional image in the drug selection field 102 to adjust the brightness of the functional image. This allows the control unit 51 of the information processing device 50 to accept the selection of the radiopharmaceutical used in the acquired functional image. By operating the "cancel selection" icon 103, the selected drug can be deselected.

[0035] In image interpretation handbooks and guidelines, specific reference regions are defined according to the type of radiopharmaceutical used in PET examinations, so the reference region is automatically selected by selecting the drug in the drug selection field 102. However, a user such as a doctor can also select a desired reference region by operating the "reference region selection" icon 104. Specifically, in any of the sagittal, coronal, and axial images, the user can specify the region to be identified as the reference region and operate the "reference region selection" icon 104. Alternatively, a predetermined operation for selecting a reference region can be performed on the image. The selected reference region can be deselected by operating the "deselect" icon 105.

[0036] The control unit 51 (display control unit 55) can adjust the brightness of the functional image according to the selected radiopharmaceutical and display it. The control unit 51 (display control unit 55) may also accept the selection of a reference region on the functional image and adjust the brightness of the functional image based on the signal value of the selected reference region and display it.

[0037] 6 is a diagram showing an example of the display on the display screen 110 after brightness adjustment. In the image area 101, functional images with adjusted brightness are displayed in the form of sagittal, coronal, and axial slices. By adjusting the brightness, the degree of accumulation of amyloid beta in the brain becomes clearer, and accurate interpretation of the intrusion into white matter and gray matter becomes possible within and between cases.

[0038] Information about the reference region can be displayed in the reference region information field 111. The control unit 51 (display control unit 55) displays information about the identified reference region. For example, if the pons (Pons) region of the brain is used as the reference region to adjust the brightness of the functional image, information such as "The brightness of the pons (Pons) has been adjusted to 00% of the maximum brightness" may be displayed.

[0039] By operating the "reference area selection" icon 104, a desired reference area can be selected, the brightness can be adjusted based on the selected reference area, and the display of the functional image can be updated. By operating the "predetermined reference area" icon 112, a default reference area (e.g., a reference area determined by a drug, a reference area predetermined by the user, etc.) can be selected, the brightness can be adjusted based on the default reference area, and the display of the functional image can be updated.

[0040] 7 is a diagram showing an example of a processing procedure by the information processing device 50. For convenience, the following description will be given assuming that the control unit 51 is the main actor in the processing. The control unit 51 acquires a functional image (S11) and aligns the acquired functional image with a template image (S12). The control unit 51 accepts the selection of a radiopharmaceutical used in the functional image (S13), and identifies a reference region based on the acquired functional image (S14).

[0041] The control unit 51 identifies a color scale corresponding to the selected radiopharmaceutical (S15), and calculates the minimum and maximum brightness values ​​based on the statistical values ​​of the signal values ​​of the reference region and the predetermined values ​​of the identified color scale (S16).The control unit 51 adjusts the brightness of the functional image based on the calculated minimum and maximum brightness values ​​(S17), and ends the process.

[0042] According to this embodiment, by using only functional images such as PET images to perform segmentation of the reference region, preprocessing of functional images required to start interpretation, including brightness adjustment, which has conventionally been done manually, can be performed fully automatically and seamlessly in accordance with interpretation guidelines. Brightness can be adjusted automatically without the need for manual adjustment by a doctor or the like.

[0043] The computer program of this embodiment causes a computer to execute the following processes: obtain a functional image by detecting radiation emitted from a radioactive drug; accept a selection of a radioactive drug used in the obtained functional image; and adjust and display the brightness of the functional image according to the selected radioactive drug.

[0044] The computer program of this embodiment causes a computer to execute a process of adjusting and displaying the brightness of an acquired functional image based on a signal value of a specific reference region on the functional image.

[0045] The computer program of this embodiment causes the computer to execute a process of adjusting and displaying the brightness of the functional image based on a predetermined value of a predetermined color scale or gray scale for the selected radiopharmaceutical and the signal value.

[0046] The computer program of this embodiment causes a computer to execute a process in which, when a functional image is input, the acquired functional image is input into a learning model that outputs a specific reference area, and a specific reference area on the functional image is acquired.

[0047] The computer program of this embodiment causes a computer to execute a process of acquiring training data including specific reference regions on functional images and structural images, and generating the learning model based on the training data so that when the functional image is input, the specific reference region included in the training data is output.

[0048] The computer program of this embodiment causes a computer to execute a process of displaying information relating to the specific reference region.

[0049] The computer program of this embodiment causes the computer to execute a process of accepting the selection of a reference area on an acquired functional image, and adjusting and displaying the brightness of the functional image based on the signal value of the selected reference area.

[0050] The information processing device of this embodiment includes an acquisition unit that detects radiation emitted from a radioactive drug and acquires an imaged functional image, a reception unit that receives a selection of a radioactive drug used in the acquired functional image, and a display unit that adjusts and displays the brightness of the functional image according to the selected radioactive drug.

[0051] The information processing method of this embodiment detects radiation emitted from a radioactive drug to obtain a functional image, accepts a selection of a radioactive drug used in the obtained functional image, and adjusts and displays the brightness of the functional image according to the selected radioactive drug. [Explanation of symbols]

[0052] 1. Communication Network 10 PET equipment 30 Image Server 40 Client Device 50 Information processing equipment 51 Control section 52 Communications Department 53 Memory 54 Pretreatment section 55 Display control unit 56 Brightness adjustment section 57 Memory section 58 Computer Programs 59 Learning Model

Claims

1. On the computer, A functional image is obtained by detecting radiation emitted from the radioactive agent; Accepting the selection of the radiopharmaceutical; adjusting and displaying the brightness of the functional image based on the selected radiopharmaceutical and the signal value of a specific reference region on the acquired functional image; A computer program that executes a process.

2. On the computer, adjusting and displaying the brightness of the functional image based on a predetermined value of a predetermined color scale or a predetermined value of a predetermined gray scale for the selected radiopharmaceutical and the signal value; The computer program product of claim 1 , which executes a process.

3. On the computer, When a functional image is input, the acquired functional image is input to a learning model that outputs a specific reference region, and a specific reference region on the functional image is acquired.

3. The computer program according to claim 1, which executes a process.

4. On the computer, obtaining training data including functional images and specific reference regions on the functional images; generating the learning model based on the training data so that, when the functional image is input, a specific reference region included in the training data is output; 4. A computer program according to claim 3, which causes a process to be executed.

5. On the computer, Displaying information about the specific reference region; 5. A computer program product according to claim 1, which causes a process to be executed.

6. an acquisition unit that detects radiation emitted from the radiopharmaceutical and acquires a functional image; a reception unit that receives the selection of the radiopharmaceutical; a display unit that adjusts and displays the brightness of the functional image based on the selected radiopharmaceutical and the signal value of a specific reference region on the acquired functional image; Equipped with Information processing device.

7. A functional image is obtained by detecting radiation emitted from the radioactive agent; Accepting the selection of the radiopharmaceutical; adjusting and displaying the brightness of the functional image based on the selected radiopharmaceutical and the signal value of a specific reference region on the acquired functional image; An information processing method that causes a computer to execute a process.

Images