Medical information processing apparatus, medical image diagnostic apparatus, method, and program

Abstract

To increase diagnostic efficiency.SOLUTION: A medical information processing apparatus includes a first acquisition unit, a second acquisition unit, and a determination unit. The first acquisition unit obtains a probability map indicating a probability relating to an affected part of a subject based on a first image. The second acquisition unit obtains part information that allows the position of the part to be specified based on a second image. The determination unit associates at least one region included in the probability map with the part specified by the part information and applies a threshold corresponding to the associated part to the region, to determine an extraction area that is to be extracted from the probability map.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a medical information processing apparatus, a medical image diagnostic apparatus, a method, and a program. [Background technology]

[0002] In PET (Positron Emission Tomography) scans, physicians typically interpret PET images by checking the location of radioisotope (RI) tracer (radioactive drug) accumulation. However, physicians often overlook areas of low RI tracer accumulation in the early stages of tumor metastasis. If an area of ​​low RI tracer accumulation indicates an early metastasis, it may be a metastasis to a site that is likely to affect progression staging or future quality of life (QOL). To assist physicians in interpretation and prevent oversights, applications exist that use deep learning (DL) technology to extract (segment) areas of abnormal RI tracer accumulation. However, learning tends to be heavily influenced by areas of high accumulation, making it technically difficult to extract areas of weak accumulation. For these reasons, even if an area of ​​low RI tracer accumulation is a risk area for tumor metastasis, it is desirable to identify and present that area to physicians. If it becomes possible to extract areas at risk for tumor metastasis, doctors will be less likely to overlook early metastasis, improving the efficiency of their PET image diagnoses. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-61290 Summary of the Invention [Problem to be solved by the invention]

[0004] The problem to be solved by the present invention is to improve the efficiency of diagnosis. [Means for solving the problem]

[0005] A medical image processing device according to an embodiment includes a first acquisition unit, a second acquisition unit, and a determination unit. The first acquisition unit acquires a probability map indicating the probability of a lesion site in a subject based on a first image. The second acquisition unit acquires site information that allows the location of the site to be identified based on a second image. The determination unit associates at least one region included in the probability map with a site identified by the site information, and determines an extraction region to be extracted from the probability map by applying a threshold value corresponding to the associated site to the region. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a medical information processing system according to the first embodiment. [Figure 2] FIG. 2 is a flowchart showing an example of the flow of processing executed by the medical image processing apparatus according to the first embodiment. [Figure 3] FIG. 3 is a diagram for explaining an example of the processing in step S103 and step S104 according to the first embodiment. [Figure 4] FIG. 4 is a diagram for explaining an example of the process of step S107 according to the first embodiment. [Figure 5] FIG. 5 is a diagram for explaining an example of the process of step S108 according to the first embodiment. [Figure 6] FIG. 6 is a diagram showing an example of the overall configuration of the PET-CT apparatus according to the second embodiment. [Figure 7] FIG. 7 is a block diagram showing an example of the configuration of a console device according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, embodiments of a medical information processing apparatus, a medical image diagnostic apparatus, a method, and a program will be described in detail with reference to the accompanying drawings.

[0008] (First embodiment) In the first embodiment, a medical information processing system 100 including a medical information processing device 30 will be described as an example. For example, as shown in Fig. 1, the medical information processing system 100 includes a PET-CT device 10, a database 20, and the medical information processing device 30. Fig. 1 is a block diagram showing an example of the configuration of the medical information processing system 100 according to the first embodiment.

[0009] 1, a PET-CT (Computed Tomography) device 10, a database 20, and a medical information processing device 30 are connected via a network 90. ​​Here, the network 90 may be configured as a closed local network within a hospital, or may be a network via the Internet. For example, the network 90 includes a LAN (Local Area Network) or a WAN (Wide Area Network).

[0010] The PET-CT device 10 is a device that collects PET images and CT images from a subject. The subject is administered a drug labeled with a positron-emitting nuclide (RI tracer (radiopharmaceutical)). The PET image is a functional image, and the CT image is a morphological image.

[0011] The database 20 is a storage device that stores various data, and is realized by computer equipment such as a server or a workstation. The database 20 may be a server of an information management system such as a Radiology Information System (RIS), a Hospital Information System (HIS), or a Picture Archiving and Communication System (PACS). For example, the database 20 includes an image storage device that stores PET images and CT images acquired by the PET-CT device 10. Although a single database 20 is shown in FIG. 1, the database 20 may be realized by a combination of multiple storage devices.

[0012] The medical information processing device 30 supports a doctor's diagnosis through processing by the processing circuitry 35. In this embodiment, even if an area has a low accumulation of RI tracer, if that area is a risk area for tumor metastasis, the medical information processing device 30 can extract that area and present it to the doctor. As a result, it is possible to prevent doctors from overlooking early metastasis, and the efficiency of doctors' PET image diagnoses can be improved.

[0013] For example, as shown in FIG. 1, the medical information processing device 30 includes a communication interface 31, an input interface 32, a display 33, a memory , and a processing circuit .

[0014] The communication interface 31 is configured by, for example, a network card such as a LAN card, a network adapter, etc. The communication interface 31 transmits and receives various information to and from devices connected via the network 90 under the control of the processing circuit 35.

[0015] The input interface 32 accepts various input operations from the user, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuit 35. For example, the input interface 32 may be implemented by a mouse, keyboard, trackball, switch, button, joystick, a touchpad that allows input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, a voice input circuit, etc. The input interface 32 may also be configured as a tablet terminal or the like that can wirelessly communicate with the medical information processing device 30. The input interface 32 may also be a circuit that accepts input operations from the user using motion capture. For example, the input interface 32 can accept the user's body movements, gaze, etc. as input operations by processing signals acquired via a tracker and images collected about the user. The input interface 32 is not limited to those that include physical operating components such as a mouse and keyboard. For example, an example of the input interface 32 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the medical information processing device 30 and outputs this electrical signal to the processing circuit 35.

[0016] The display 33 displays various types of information and images. For example, the display 33 displays PET images and CT images captured by the PET-CT device 10 under the control of the processing circuitry 35. Furthermore, for example, the display 33 displays a GUI (Graphical User Interface) for receiving various instructions, settings, etc. from a user via the input interface 32. For example, the display 33 is a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 33 may be a desktop type, or may be configured as a tablet terminal or the like capable of wireless communication with the main body of the medical information processing device 30. The display 33 is an example of a display unit.

[0017] The medical information processing device 30 may include a projector instead of or in addition to the display 33. The projector can project onto a screen, wall, floor, etc. under the control of the processing circuitry 35. For example, the projector can project onto any plane, object, space, etc. by projection mapping. Such a projector is an example of a display unit.

[0018] The memory 34 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. For example, the memory 34 stores various data (image data) such as PET images and CT images. The memory 34 also stores programs that enable circuits included in the medical information processing device 30 to realize various functions. The memory 34 may also be realized by a group of servers (cloud) connected to the medical information processing device 30 via a network 90.

[0019] The memory 34 also stores a table in which a plurality of regions are registered in association with a threshold value of the probability that the region is a tumor. Here, the regions include various organs (heart, lungs, liver, pancreas, etc.), the outer part of the skull, mammary glands, thyroid gland, aortic lymph nodes near the large intestine, etc.

[0020] The thresholds registered in the table will now be described. In this embodiment, a probability map is acquired by the second acquisition function 352 as described below. The probability map is data indicating the probability that each position (e.g., each pixel) in a PET image is a tumor. The probability is a value within a range of 0% to 100%. When extracting (segmenting) a tumor region from a PET image, a region in the probability map with a probability equal to or greater than a threshold is extracted as a tumor region from the entire region of the PET image.

[0021] Here, the probability that a region where the RI tracer accumulation is weak is a tumor is relatively low. Therefore, the probability of a region where the RI tracer accumulation is weak tends to be below the threshold. Therefore, a region where the RI tracer accumulation is weak tends to be difficult to extract as a tumor region. However, even if a region where the RI tracer accumulation is weak is a region where there is a risk of tumor metastasis, it is desirable to extract the region as a tumor region. Therefore, in this embodiment, to facilitate extraction of such a region, the user registers the region and a relatively low threshold in a table via the input interface 32. In this way, the region and the threshold are registered in association with each other for each region in the table.

[0022] The memory 34 also stores a minimum threshold value (minimum threshold value) for the probability of being a tumor. The minimum threshold value is smaller than the threshold values ​​registered in the table. In other words, the absolute value of the minimum threshold value is smaller than the absolute value of the threshold values ​​registered in the table.

[0023] The processing circuit 35 includes a first acquisition function 351, a second acquisition function 352, a decision function 353, and a display control function 354. The first acquisition function 351 is, for example, an example of a first acquisition unit. The second acquisition function 352 is, for example, an example of a second acquisition unit. The decision function 353 is, for example, an example of a decision unit. The display control function 354 is, for example, an example of a display control unit.

[0024] 1, each processing function is stored in the form of a computer-executable program in a memory 34. The processing circuitry 35 is a processor that realizes the function corresponding to each program by reading and executing each program from the memory 34. In other words, the processing circuitry 35 in a state in which a program has been read has various functions corresponding to the read program.

[0025] 1, the first acquisition function 351, the second acquisition function 352, the decision function 353, and the display control function 354 are realized by a single processing circuit 35. However, the processing circuit 35 may be configured by combining a plurality of independent processors, and each processor may execute a program to realize the functions. Furthermore, each processing function of the processing circuit 35 may be realized by being distributed or integrated as appropriate in a single or multiple processing circuits.

[0026] The processing circuitry 35 may also realize its functions by using a processor in an external device connected via the network 90. ​​For example, the processing circuitry 35 reads and executes a program corresponding to each function from the memory 34, and realizes each function shown in Fig. 1 by using a group of servers (cloud) connected to the medical information processing device 30 via the network 90 as a computational resource.

[0027] 2 is a flowchart showing an example of the flow of processing executed by the medical information processing apparatus 30 according to the first embodiment. As shown in Fig. 2, the first acquisition function 351 acquires PET images obtained by imaging a subject to be examined from the PET-CT apparatus 10 or the database 20 (step S101).

[0028] Furthermore, the first acquisition function 351 acquires a probability map by inference processing (step S102). For example, the first acquisition function 351 acquires the probability map using the PET image acquired in step S101 and a trained model (not shown) stored in the memory 34. The trained model is configured to output a probability map indicating the probability of a tumor for each position (e.g., each pixel) of the input PET image when the PET image is input. The first acquisition function 351 inputs the PET image to the trained model and acquires the probability map output from the trained model. Note that the first acquisition function 351 may acquire the probability map by generating the probability map from the PET image by a predetermined process other than the inference process.

[0029] Thus, in step S102, the first acquisition function 351 acquires a probability map indicating the probability of a tumor in the subject based on the PET image. The PET image is, for example, an example of a first image. The tumor is, for example, an example of a lesion site.

[0030] Next, the decision function 353 extracts at least one independent region (step S104) by extracting regions of the probability map that exceed a minimum threshold stored in the memory 34 (step S103).

[0031] 3 is a diagram illustrating an example of the processing in step S103 and step S104 according to the first embodiment. A PET image 50 acquired in step S101 is shown in FIG. The probability values ​​correspond to various colors. For example, 0% is purple, 60% is yellow, 80% is orange, and 100% is red. The colors corresponding to the probability values ​​are not limited to these.

[0032] If the minimum threshold is 20%, in step S103, regions 61 and 62 exceeding 20% ​​are extracted from the entire region of the probability map, and in step S104, the regions 61 and 62 become independent regions 61 and 62. The probability indicated by each position of the independent region 61 is greater than 20% and less than or equal to 100%. In contrast, the probability indicated by each position of the independent region 62 is greater than 60% and less than or equal to 100%.

[0033] The reason for extracting independent regions using the minimum threshold value in steps S103 and S104 is to clarify the range of the region to be linked to the part in step S107, which will be described later.

[0034] 2, the second acquisition function 352 acquires CT images obtained by imaging the subject to be examined from the PET-CT device 10 or the database 20 (step S105). That is, the subject depicted in the PET image acquired in step S101 is the same as the subject depicted in the CT image acquired in step S105.

[0035] Then, the second acquisition function 352 acquires the region information by generating the region information using the CT image using known technology (step S106). Region information is, for example, information indicating the range of various regions, or information indicating the positions of anatomical landmarks of various regions. When the region information indicates the positions of anatomical landmarks of various regions, a predetermined range including the positions of the landmarks indicated by the region information is treated as the region range. Therefore, region information, regardless of the information, is information that can identify the position of the region. In this way, the second acquisition function 352 acquires region information that can identify the position of the region based on the CT image (second image). The CT image is, for example, an example of the second image.

[0036] Then, the determination function 353 links the extracted independent regions with the positions (ranges) of the regions indicated by the region information (step S107). As a result, each independent region is linked with each region. For example, the independent region 61 is linked with the outer part of the skull. Also, for example, the independent region 62 is linked with the thyroid gland. In this way, the determination function 353 links at least one region included in the probability map with the region identified by the region information. Note that the at least one region linked with the region may be multiple regions or may be one region. Also, the determination function 353 applies a minimum threshold to the probability map to link the independent regions extracted from the probability map with the region identified by the region information. The minimum threshold is, for example, an example of a first threshold. Also, the independent region is, for example, an example of a region.

[0037] Then, the determination function 353 refers to the contents of the table and determines, for each part, a region to be extracted (extraction region (segmentation region)) using a threshold value corresponding to the part (step S107).

[0038] FIG. 4 is a diagram illustrating an example of the processing of step S107 according to the first embodiment. For example, as shown in FIG. 4, the determination function 353 extracts, from the entire independent region 61, a region corresponding to a threshold of 60% that corresponds to the outer part of the skull as the extraction region 63. Furthermore, the determination function 353 extracts, from the entire independent region 62, a region corresponding to a threshold of 80% that corresponds to the thyroid gland as the extraction region 64. In this manner, the determination function 353 determines an extraction region to be extracted from the probability map by applying a threshold corresponding to the linked region to the region. Furthermore, the determination function 353 determines an extraction region by applying a threshold to the independent region as a threshold corresponding to the linked region. Such a threshold corresponds to each of the multiple regions linked to each of the multiple independent regions. Such a threshold is, for example, an example of a second threshold. In this embodiment, even if an area has low accumulation of the RI tracer, if that area is a region that poses a risk of tumor metastasis, the determination function 353 can determine the area of ​​that area as an extraction region for the physician to grasp. As a result, the medical information processing device 30 according to this embodiment allows the doctor to understand the extracted area displayed on the display 33, thereby preventing the doctor from overlooking early metastasis, thereby improving the efficiency of the doctor's diagnosis of PET images.

[0039] Then, the display control function 354 controls the display 33 to superimpose the extracted region on the PET image and highlight the extracted region according to the threshold value (step S108), and the processing shown in FIG. 2 ends.

[0040] FIG. 5 is a diagram illustrating an example of the processing of step S108 according to the first embodiment. For example, as shown in FIG. 8, the display control function 354 controls the display 33 to display the PET image 50 on which the extraction regions 63 to 70 are superimposed. Here, the extraction regions 63, 65, and 66 correspond to a threshold of 60%. The extraction regions 64, 67, 68, and 70 correspond to a threshold of 80%. The extraction region 69 corresponds to a threshold of 40%. In this case, for example, the display control function 354 controls the display 33 so that the displayed extraction regions 63, 65, and 66 are yellow. For example, the display control function 354 controls the display 33 so that the displayed extraction regions 64, 67, 68, and 70 are green. For example, the display control function 354 controls the display 33 so that the displayed extraction region 69 is orange. In this manner, the display control function 354 highlights the extraction regions on the display 33. By displaying the extracted region in a color according to the threshold value, the doctor can intuitively grasp the minimum value of the probability indicated by the extracted region.

[0041] The above has described the first embodiment. As described above, the medical information processing apparatus 30 according to the first embodiment can improve the efficiency of diagnosis.

[0042] (Second embodiment) In the first embodiment, a case has been described in which the medical information processing device 30 executes the processing shown in Fig. 2. However, the PET-CT device 10, which is a medical image diagnostic device, may execute processing similar to the processing shown in Fig. 2 to improve the efficiency of diagnosis by doctors. Therefore, such an embodiment will be described as the second embodiment. In the description of the second embodiment, configurations different from the first embodiment will be mainly described, and a description of configurations similar to the first embodiment may be omitted.

[0043] Fig. 6 is a diagram showing an example of the overall configuration of a PET-CT apparatus 10 according to the second embodiment. As shown in Fig. 6, the PET-CT apparatus 10 includes a PET gantry device 1, a CT gantry device 2, a bed 3, and a console device 4. An RI tracer is administered to a subject P. The PET-CT apparatus 10 is an example of a medical image diagnostic apparatus.

[0044] The PET gantry device 1 is a device that detects pairs of gamma rays (pair annihilation gamma rays) emitted from living tissue that has taken in positron-emitting nuclides, and generates counting information from the gamma ray detection signals, thereby collecting counting information. The counting information is stored in a memory 41 (described later) of the console device 4.

[0045] The CT gantry device 2 is a device that generates X-ray projection data that is the basis of a CT image by detecting X-rays that have passed through the subject P. The CT gantry device 2 can also generate X-ray projection data that is the basis of a two-dimensional or three-dimensional scanogram.

[0046] The bed 3 is a bed on which the subject P rests, and includes a tabletop 36, a support frame 37, and a bed device 38. The bed 3 sequentially moves the subject P to the imaging ports of the CT gantry device 2 and the PET gantry device 1 based on instructions from the operator of the PET-CT device 10 received via the console device 4. That is, the PET-CT device 10 controls the bed 3 to first capture CT images and then capture PET images. Note that while FIG. 6 shows an example in which the CT gantry device 2 is disposed on the bed 3 side, the embodiment is not limited to this, and the PET gantry device 1 may also be disposed on the bed 3 side.

[0047] The bed 3 uses a driving mechanism (not shown) to move the top board 36 and the support frame 37 in the central axis direction of the detector field of view of the CT gantry device 2 and the PET gantry device 1. In other words, the bed 3 moves the top board 36 and the support frame 37 in a direction along the longitudinal direction of the top board 36 and the support frame 37.

[0048] The console device 4 is a device that receives instructions from an operator and controls processing by the PET-CT device 10. FIG. 7 is a diagram showing an example of the configuration of the console device 4 according to the first embodiment. As shown in FIG. 7, the console device 4 has a memory 41, a display 42, an input interface 43, and a processing circuit 44.

[0049] The display 42 displays various images and information under the control of a display control function 445, which will be described later. For example, the display 42 has the same configuration as the display 33 described above.

[0050] The input interface 43 has the same configuration as the input interface 32 described above, and accepts instructions and operations from the user.

[0051] The processing circuit 44 executes various processes. For example, as shown in FIG. 7, the processing circuit 44 includes a control function 441, a first acquisition function 442, a second acquisition function 443, a decision function 444, and a display control function 445. Here, for example, the processing functions executed by the first acquisition function 442, the second acquisition function 443, the decision function 444, and the display control function 445, which are components of the processing circuit 44 shown in FIG. 7, are recorded in the memory 41 in the form of programs executable by a computer. The processing circuit 44 is a processor that reads each program from the memory 41 and executes it to realize the function corresponding to each program. In other words, the processing circuit 44 in a state in which each program has been read has each function shown in the processing circuit 44 in FIG. 7.

[0052] The processing circuitry 44 further has a PET image generation function for generating a PET image using counting information collected by the PET gantry device 1, and a CT image generation function for generating a CT image using X-ray projection data generated by the CT gantry device 2. The PET image generation function stores the generated PET image in the memory 41. The CT image generation function also stores the generated CT image in the memory 41.

[0053] The term "processor" refers to a circuit such as a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)). The processor realizes its function by reading and executing a program stored in the memory 41. Instead of storing the program in the memory 41, the processor may be configured so that the program is directly embedded in its circuit. In this case, the processor realizes its function by reading and executing the program embedded in the circuit. Each processor in this embodiment is not limited to being configured as a single circuit, but may also be configured as a single processor by combining multiple independent circuits.

[0054] The control function 441 controls the operation of the PET-CT device 10. For example, the control function 441 receives instructions from a user via the input interface 43, and controls the PET gantry device 1, the CT gantry device 2, and the bed 3 in accordance with the received instructions to perform various processes such as scanogram collection, scanning (actual imaging), image reconstruction, image generation, and image display. The control function 441 is, for example, an example of a control unit.

[0055] The first acquisition function 442 has the same function as the first acquisition function 351. For example, the first acquisition function 442 acquires PET images stored in the memory 41 and performs various processes using the acquired PET images. The second acquisition function 443 has the same function as the second acquisition function 352. For example, the second acquisition function 443 acquires CT images stored in the memory 41 and performs various processes using the acquired CT images. The decision function 444 has the same function as the decision function 353. The display control function 445 has the same function as the display control function 354. However, the display control function 445 displays various images and various information on the display 42 instead of the display 33.

[0056] The above describes the second embodiment. The PET-CT apparatus 10 according to the second embodiment can improve the efficiency of diagnosis, similar to the medical information processing apparatus 30 according to the first embodiment.

[0057] In the above-described embodiments, the case where the PET-CT device 10 is used has been described. However, a PET-MR (Magnetic Resonance) device may be used instead of the PET-CT device 10. In this case, an MR image, which is a morphological image generated by the PET-MR device, is used instead of a CT image.

[0058] The program executed by the processor may be provided by being pre-installed in a ROM (Read Only Memory), a storage unit, etc. The program may be provided by being stored in a computer-readable storage medium such as a CD (Compact Disk)-ROM, a FD (Flexible Disk), a CD-R (Recordable), or a DVD (Digital Versatile Disk) in a format that can be installed or executed by these devices. The program may also be provided or distributed by being stored on a computer connected to a network such as the Internet and downloaded via the network.

[0059] Furthermore, the components of each device illustrated in the above-described embodiments are merely functional concepts and do not necessarily have to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to that illustrated, and all or part of the devices can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Furthermore, all or any part of the processing functions performed by each device can be realized by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0060] According to at least one of the embodiments described above, it is possible to improve the efficiency of diagnosis.

[0061] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention described in the claims and their equivalents. [Explanation of symbols]

[0062] 30 Medical information processing device 351 First Acquisition Function 352 Second Acquisition Function 353 Decision Function 354 Display Control Function

Claims

1. a first acquisition unit that acquires a probability map indicating a probability of a lesion site in the subject based on the first image; a second acquisition unit that acquires body part information that can identify the position of the body part based on the second image; a determination unit that determines an extraction region to be extracted from the probability map by linking at least one region included in the probability map with a region specified by the region information and applying a threshold value corresponding to the linked region to the region; and A medical information processing device comprising:

2. 2. The medical information processing device according to claim 1, wherein the determination unit applies a first threshold to the probability map to link the region extracted from the probability map to a region identified by the region information, and determines the extracted region by applying a second threshold to the region as a threshold corresponding to the linked region.

3. The medical image processing apparatus according to claim 1 , further comprising a display control unit that causes a display unit to highlight the extracted region determined by the determination unit.

4. The medical image processing apparatus according to claim 2 , wherein the absolute value of the first threshold is smaller than the absolute value of the second threshold.

5. the at least one region is a plurality of regions, The medical image processing apparatus according to claim 2 , wherein the second threshold value is a threshold value corresponding to each of a plurality of parts linked to each of the plurality of regions.

6. a first acquisition unit that acquires a probability map indicating a probability of a lesion site in the subject based on the first image; a second acquisition unit that acquires body part information that can identify the position of the body part based on the second image; a determination unit that determines an extraction region to be extracted from the probability map by linking at least one region included in the probability map with a region specified by the region information and applying a threshold value corresponding to the linked region to the region; and A medical image diagnostic device comprising:

7. obtaining a probability map indicating a probability of a lesion location in the subject based on the first image; acquiring part information that can identify the position of the part based on the second image; linking at least one region included in the probability map with a region identified by the region information; determining an extracted region of interest to be extracted from the probability map by applying a threshold value to the region corresponding to the associated site.

8. On the computer, obtaining a probability map indicating a probability of a lesion location in the subject based on the first image; acquiring region information that can identify the position of the region based on the second image; Linking at least one region included in the probability map with a region specified by the region information. A program for executing a process of determining an extracted region that is a target to be extracted from the probability map by applying a threshold corresponding to the linked portion to the region.

Images