Medical image processing equipment

Abstract

To support check of filtration within the vertebral canal.SOLUTION: A medical image processing device according to the embodiment comprises an extraction part, a derivation part, and a display control part. The extraction part extracts a first region being a region of the vertebral canal included in a medical image. The derivation part derives an index concerning the degree of infiltration in the vertebral canal based on the first region and a second region in which a pixel value is a threshold or more in the region of the vertebral canal. The display control part causes a display part to display the index.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to a medical image processing apparatus. [Background technology]

[0002] The spinal cord is a bundle of nerves located behind the vertebral bodies, and the spinal canal is a tubular structure that surrounds the spinal cord. If cancer invades the spinal canal, the spinal cord can be compressed within the canal, potentially resulting in paralysis of the limbs or trunk. Early detection of spinal canal invasion is important, primarily to maintain the patient's quality of life (QOL). However, confirmation of spinal canal invasion is currently performed visually by doctors, and the development of technology to assist this process is desired. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2010-131127 Summary of the Invention [Problem to be solved by the invention]

[0004] One of the problems that the embodiments disclosed in this specification and drawings aim to solve is to assist in the confirmation of spinal canal invasion. However, the problems solved by the embodiments disclosed in this specification and drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0005] The medical image processing device according to this embodiment includes an extraction unit, a derivation unit, and a display control unit. The extraction unit extracts a first region that is a region of the spinal canal included in a medical image. The derivation unit derives an index relating to the degree of infiltration into the spinal canal based on the first region and a second region in the spinal canal region where pixel values ​​are equal to or greater than a threshold. The display control unit displays the index on the display unit. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a medical image processing system including a medical image processing apparatus according to this embodiment. [Figure 2] FIG. 2 is a diagram showing an example of the configuration of a medical image processing apparatus according to this embodiment. [Figure 3] FIG. 3 is a flowchart showing the procedure of processing by the medical image processing apparatus according to this embodiment. [Figure 4] FIG. 4 is a diagram showing an example of an axial cross-sectional image acquired by the acquisition function of the medical image processing apparatus according to this embodiment. [Figure 5] FIG. 5 is a diagram showing an example of an extracted image generated by the extraction function of the medical image processing apparatus according to this embodiment. [Figure 6] FIG. 6 is a diagram showing a display example by the display control function in the medical image processing apparatus according to this embodiment. [Figure 7] FIG. 7 is an enlarged view of the image shown in FIG. [Figure 8] FIG. 8 is an enlarged view of the image shown in FIG. [Figure 9] FIG. 9 is an enlarged view of the image shown in FIG. [Figure 10] FIG. 10 is an enlarged view of the image shown in FIG. [Figure 11] FIG. 11 is an enlarged view of the image shown in FIG. [Figure 12] FIG. 12 is an enlarged view of the image shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, an embodiment of a medical image processing apparatus will be described in detail with reference to the drawings, taking a medical image processing system including the medical image processing apparatus as an example.

[0008] FIG. 1 is a diagram showing an example of the configuration of a medical image processing system 1 including a medical image processing device 100 according to this embodiment. The medical image processing system 1 shown in FIG. 1 includes the medical image processing device 100, a medical image diagnostic device 200, and an image storage device 300. The medical image processing device 100 is connected to the medical image diagnostic device 200 and the image storage device 300 via a network 2 such as an in-hospital LAN (Local Area Network) installed in a hospital. Here, each device is capable of communicating with each other directly or indirectly. For example, if a PACS (Picture Archiving and Communication System) is introduced in the medical image processing system 1, each device transmits and receives medical images and the like to and from each other in accordance with the DICOM (Digital Imaging and Communications in Medicine) standard.

[0009] The medical image diagnostic device 200 is an X-ray diagnostic device, an X-ray CT (Computed Tomography) device, an MRI (Magnetic Resonance Imaging) device, an ultrasound diagnostic device, a SPECT (Single Photon Emission Computed Tomography) device, a PET (Positron Emission Computed Tomography) device, a SPECT-CT device in which a SPECT device and an X-ray CT device are integrated, a PET-CT device in which a PET device and an X-ray CT device are integrated, or a group of these devices, etc. The medical image diagnostic device 200 is capable of generating two-dimensional medical images, three-dimensional medical images (volume data), time-series two-dimensional medical images, and time-series three-dimensional medical images.

[0010] Here, the medical image diagnostic apparatus 100 acquires medical images by capturing images of a subject. For example, an X-ray CT apparatus, which is the medical image diagnostic apparatus 100, rotates an X-ray tube and an X-ray detector around a subject injected with a contrast agent, detects X-rays that have passed through the subject, and acquires projection data. Based on the acquired projection data, the X-ray CT apparatus generates two-dimensional CT images, three-dimensional CT images (volume data), two-dimensional CT images in time series, and three-dimensional CT images in time series. Alternatively, based on the acquired projection data, the X-ray CT apparatus generates multiple two-dimensional CT images along a predetermined direction. For example, the X-ray CT apparatus generates two-dimensional CT images of multiple axial cross sections along the body axis direction.

[0011] The medical image diagnostic apparatus 200 transmits the generated medical image to the image storage apparatus 300. When transmitting the medical image to the image storage apparatus 300, the medical image diagnostic apparatus 200 transmits additional information such as a patient ID for identifying the patient, an examination ID for identifying the examination, an apparatus ID for identifying the medical image diagnostic apparatus 200, and a series ID for identifying one imaging session performed by the medical image diagnostic apparatus 200.

[0012] The image storage device 300 is a database that stores medical images. Specifically, the image storage device 300 includes a memory circuit, and stores medical images transmitted from the medical image diagnostic device 200 by storing the medical images in the memory circuit. The memory circuit of the image storage device 300 is, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The medical images stored in the image storage device 300 are stored in association with a patient ID, examination ID, device ID, series ID, etc. Therefore, the medical image processing device 100 can retrieve required medical images from the image storage device 300 by performing a search using the patient ID, examination ID, device ID, series ID, etc.

[0013] The medical image processing device 100 is an image processing device that performs image processing on medical images, and is, for example, a workstation, an image server or viewer of a PACS (Picture Archiving and Communication System), various devices of an electronic medical record system, etc. The medical image processing device 100 performs various processes on medical images acquired from a medical image diagnostic device 200 or an image storage device 300.

[0014] 2 is a diagram showing an example of the configuration of a medical image processing apparatus 100 according to this embodiment. As shown in FIG. 2, the medical image processing apparatus 100 has an input interface 110, displays 120 and 123, a communication interface 130, a memory circuitry 140, and a processing circuitry 150.

[0015] The input interface 110 has a pointing device such as a mouse, a keyboard, etc., and receives inputs of various operations for the medical image processing apparatus 100 from a user, and transfers the instructions and setting information received from the user to the processing circuitry 150.

[0016] The display 120 is a monitor viewed by the user, and under the control of the processing circuitry 150, displays images to the user and displays a GUI (Graphical User Interface) for receiving various instructions and settings from the user via the input interface 110. The communication interface 130 is a NIC (Network Interface Card) or the like, and communicates with other devices. The display 120 is an example of a display unit. The memory circuitry 140 is, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk.

[0017] The processing circuitry 150 controls the components of the medical image processing apparatus 100. For example, as shown in FIG. 2, the processing circuitry 150 executes an acquisition function 151, an extraction function 152, a derivation function 153, and a display control function 154. Here, for example, the processing functions executed by the acquisition function 151, the extraction function 152, the derivation function 153, and the display control function 154, which are components of the processing circuitry 150, are recorded in the storage circuitry 140 in the form of computer-executable programs. The processing circuitry 150 is a processor that realizes the function corresponding to each program by reading and executing each program from the storage circuitry 140. In other words, the processing circuitry 150 in a state in which each program has been read has each function shown in the processing circuitry 150 in FIG. 2. The acquisition function 151, the extraction function 152, the derivation function 153, and the display control function 154 are examples of an acquisition unit, an extraction unit, a derivation unit, and a display control unit, respectively.

[0018] The term "processor" used in the above description 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)). If the processor is a CPU, for example, the processor realizes its function by reading and executing a program stored in the memory circuit 140. On the other hand, if the processor is an ASIC, for example, the program is directly embedded in the processor circuit instead of storing the program in the memory circuit 140. Note that each processor in this embodiment is not limited to being configured as a single circuit, but may be configured as a single processor by combining multiple independent circuits to realize its function. Furthermore, multiple components in FIG. 2 may be integrated into a single processor to realize its function.

[0019] The overall configuration of the medical image processing system 1 including the medical image processing device 100 according to this embodiment has been described above. With this configuration, the medical image processing device 100 includes an extraction function 152, a derivation function 153, and a display control function 154 to assist in the confirmation of spinal canal invasion. The extraction function 152 extracts a first region, which is the region of the spinal canal included in the medical image. The derivation function 153 derives an index relating to the degree of invasion into the spinal canal based on the first region and a second region in the spinal canal region where the pixel value is equal to or greater than a threshold. The display control function 154 displays the index on the display 120.

[0020] Below, each function of the medical image processing apparatus 100 will be specifically described with reference to Fig. 3. Fig. 3 is a flowchart showing the procedure of processing by the medical image processing apparatus 100 according to this embodiment.

[0021] 3 is a step in which the processing circuitry 150 calls and executes a program corresponding to the acquisition function 151 from the storage circuitry 150. In step S101, the acquisition function 151 accepts a user operation of the input interface 110 and acquires a medical image from the medical image diagnostic device 200 or the image storage device 300.

[0022] The medical image is an image including the spinal canal of the subject, and is a plurality of cross-sectional images along a predetermined direction. For example, the medical image is a plurality of axial cross-sectional images along the body axis direction. Fig. 4 is a diagram showing an example of an axial cross-sectional image 400 acquired by the acquisition function 151. The acquisition function 151 acquires a plurality of axial cross-sectional images 400 as medical images.

[0023] 3 is a step executed by the processing circuitry 150 when the processing circuitry 150 calls up a program corresponding to the extraction function 152 from the storage circuitry 150. In step S102, the extraction function 152 extracts a spinal canal region from each of the multiple axial cross-sectional images 400 to generate an extracted image.

[0024] FIG. 5 is a diagram showing an example of an extracted image 500 generated by the extraction function 152. For example, if the medical image is a three-dimensional CT (Computed Tomography) image, the extraction function 152 extracts the spinal canal region by binarization processing using a threshold value set based on the CT value. The CT value is an example of a pixel value. Specifically, the extraction function 152 binarizes the axial cross-sectional image 400 to extract the spinal canal region. Then, the extraction function 152 generates an extracted image 500 representing the extracted spinal canal.

[0025] Step S103 in FIG. 3 is a step in which the processing circuitry 150 calls a program corresponding to the derivation function 153 from the storage circuitry 150 and executes the program.

[0026] In step S103, the derivation function 153 derives an index relating to the degree of infiltration into the spinal canal based on the first region and the second region in which the pixel value of each extracted image 500 is equal to or greater than the first threshold.

[0027] For example, if the medical image is a three-dimensional CT image and infiltration has occurred in the spinal canal of the subject, the pixel values ​​(CT values) of the area where infiltration may have occurred will be higher than those of the area where infiltration has not occurred. Therefore, as shown in Figure 5, the derivation function 153 derives an index relating to the degree of infiltration in the spinal canal based on a first area 510 which is the area of ​​the spinal canal and a second area 520 where the pixel values ​​are equal to or greater than a first threshold in each extracted image 500.

[0028] Here, the first threshold may be a value set in advance, or may be a value calculated by the derivation function 153 for each subject. For example, when the derivation function 153 calculates the first threshold, the derivation function 153 generates a histogram of pixel values ​​of the extracted image 500 of a subject with infiltration into the spinal canal. In the generated histogram, when the horizontal axis represents pixel values ​​and the vertical axis represents frequency, two peaks appear: a normal portion where no infiltration has occurred, and an abnormal portion where infiltration may have occurred. Here, the derivation function 153 calculates a pixel value that can separate the two peaks as the first threshold.

[0029] The first threshold may also be determined according to the degree of progression of the cancer stage, etc. For example, when the derivation function 153 determines the first threshold according to the cancer stage, subjects (patients) may be grouped by stage, and the first threshold may be set for each group. Here, the first threshold set for each group is determined according to the patient's attributes, such as gender and age.

[0030] For example, the index derived by the derivation function 153 is a numerical value representing the area ratio between the first region 510 and the second region 520. Specifically, the derivation function 153 calculates the area of ​​the first region 510 and the area of ​​the second region 520 in each extracted image 500. Next, the derivation function 153 calculates an area ratio representing the ratio between the area of ​​the first region 510 and the area of ​​the second region 520. For example, the area ratio is calculated by dividing the second region 520 (the area of ​​pixels equal to or greater than the first threshold) by the first region 510 (the area of ​​the spinal canal region). Then, the derivation function 153 derives an index relating to the degree of infiltration into the spinal canal based on the calculated area ratio. A higher value of this index represents a higher degree of infiltration, and a lower value represents a lower degree of infiltration.

[0031] Alternatively, the derivation function 153 may determine whether the area ratio is equal to or greater than a second threshold and output the determination result. For example, if the area ratio is equal to or greater than the second threshold, the derivation function 153 outputs a determination result indicating that there is a high possibility that infiltration has occurred, and if the area ratio is smaller than the second threshold, the derivation function 153 outputs a determination result indicating that there is a low possibility that infiltration has occurred. Note that the derivation function 153 may output both a numerical value representing the area ratio and information representing the determination result of the area ratio as indices.

[0032] Here, pixels whose pixel values ​​are equal to or greater than the first threshold may exist in clusters or in a scattered manner. It is assumed that when infiltration occurs, pixels whose pixel values ​​are equal to or greater than the first threshold exist in clusters, while when noise occurs, pixels whose pixel values ​​are equal to or greater than the first threshold exist in a scattered manner. Therefore, in step S103, the derivation function 153 further derives an index relating to the degree of infiltration in the spinal canal based on the spatial distribution of the pixels constituting the second region 520 of each extracted image 500.

[0033] Specifically, the derivation function 153 performs an area ratio calculation process, which calculates the area ratio between the first region 510 and the second region 520 of each extracted image 500, and then performs a variance calculation process. In the variance calculation process, the derivation function 153 performs contraction, expansion, and filling processes on the second region 510 to determine a third region that contains the second region 510. The derivation function 153 then divides the area of ​​the third region by the area of ​​the second region 510 to calculate a variance value that represents the degree of variation in the positions of pixels whose pixel values ​​are equal to or greater than a first threshold. For example, if the second region 520 contains a large number of pixel values ​​equal to or greater than the first threshold, the variance value is small. For example, if the variance value of the second region 520 is equal to or greater than a third threshold, the derivation function 153 determines that the second region 520 is affected by noise and is unlikely to have infiltration, and excludes it from processing. On the other hand, if the variance value of the second region 520 is smaller than the third threshold, the derivation function 153 determines that the second region 520 is a portion where infiltration may have occurred, and regards it as a processing target.

[0034] In this way, the derivation function 153 executes the above-mentioned area ratio calculation process and then executes the above-mentioned variance value calculation process, thereby making it possible to distinguish whether the second region 520 is a region where infiltration may have occurred or a region affected by noise. Note that in the above example, the derivation function 153 executes the area ratio calculation process and then executes the variance value calculation process, but this is not limiting, and the area ratio calculation process may be executed after executing the variance value calculation process.

[0035] Furthermore, it is necessary to determine whether the second region 520 is a region where infiltration may have occurred or is affected by noise, taking into consideration not only the extracted image 500 of one slice but also the extracted images 500 of the previous and following slices. In this case, in step S103, the derivation function 153 further derives an index relating to the degree of infiltration in the spinal canal based on the continuity of the second region 520 in a predetermined direction of each extracted image 500.

[0036] Specifically, the derivation function 153 performs the above-described variance value calculation process on the extracted images 500 of the preceding and following slices. For example, the derivation function 153 performs the above-described variance value calculation process on the extracted images 500 of five slices. For example, if the variance value of the second region 520 in the extracted images 500 of the preceding and following slices among the extracted images 500 of the five slices is equal to or greater than a third threshold, the derivation function 153 determines that the second region 520 in the extracted images 500 of the preceding and following slices is affected by noise and is unlikely to have infiltration, and excludes it from the processing target. On the other hand, if the variance value of the second region 520 in the extracted images 500 of the preceding and following slices is less than the third threshold, the derivation function 153 determines that the second region 520 in the extracted images 500 of the preceding and following slices is a part where infiltration is likely to have occurred, and considers it to be the processing target.

[0037] In this way, by performing the above-mentioned variance value calculation process on the extracted images 500 of the preceding and following slices, the derivation function 153 can determine whether the first region 510 of the extracted images 500 of the preceding and following slices is an area where infiltration may be occurring or is affected by noise.

[0038] 3 is a step in which the processing circuitry 150 calls and executes a program corresponding to the display control function 154 from the storage circuitry 150. In step S104, the display control function 154 causes the display 120 to display the index derived by the derivation function 153.

[0039] For example, the display control function 154 causes the display 120 to display the determination result based on the index derived by the derivation function 153, superimposed on the medical image.

[0040] Fig. 6 is a diagram showing a display example by the display control function 154. As shown in Fig. 6, the display control function 154 displays an image 550 (described below) in a first display area, which is the display area on the left side of the display 120 as seen by the user, and displays images 560 and 570 (described below) in a second display area, which is the display area on the right side of the display 120 as seen by the user. Also, as shown in Fig. 6, the display control function 154 displays images 410, 420, 430, and 540 (described below) in a third display area between the first and second display areas. Specifically, the display control function 154 divides the third display area into four parts and displays images 410, 420, 430, and 540 (described below) in the four divided display areas, respectively.

[0041] For example, suppose that the derivation function 153 determines whether the area ratio between the first region 510 and the second region 520 of the extracted image 500 is equal to or greater than a second threshold, and finds that the area ratio is equal to or greater than the second threshold. In this case, the index derived by the derivation function 153 is a determination result indicating that the area ratio is equal to or greater than the second threshold. Alternatively, the index derived by the derivation function 153 is a determination result indicating that the area ratio is equal to or greater than the second threshold and the variance value is smaller than a third threshold.

[0042] Fig. 7 is an enlarged view of the image 540 shown in Fig. 6. For example, as shown in Fig. 7, the display control function 154 displays on the display 120 a determination result 530 "ALERT" indicating that the area ratio is equal to or greater than the second threshold and the variance value is smaller than the third threshold, with the determination result 530 "ALERT" superimposed on the extracted image 500. Specifically, in the example shown in Fig. 7, the display control function 154 generates an MPR (Multi Planar Reconstruction) image 540 corresponding to the extracted image 500, and displays on the display 120 the determination result 530 "ALERT" superimposed on the MPR image 540.

[0043] 7, the display control function 154 causes the display 120 to display a color image in which a color corresponding to the area ratio value is assigned to each pixel of the second region 520 in the MPR image 540 as an index. Specifically, in the example shown in FIG. 7, when the area ratio is equal to or greater than the second threshold, the color image assigned to the second region 520 is shown in black on the drawing, but is instead displayed in a color corresponding to the area ratio value, for example, blue. This allows the physician to confirm intraspinal canal invasion by referring to the determination result 530 "ALERT" on the MPR image 540 and the color corresponding to the index.

[0044] 7 , the display control function 154 displays, on the display 120, the determination result indicating that the area ratio is equal to or greater than the second threshold as the index derived by the derivation function 153, superimposed on the MPR image 540. However, this is not limiting. For example, the display control function 154 may display, on the display 120, the area ratio as the index derived by the derivation function 153 superimposed on the MPR image 540, or may display, on the display 120, the area ratio and the determination result superimposed on the MPR image 540.

[0045] Fig. 8 is an enlarged view of the image 410 shown in Fig. 6. For example, as shown in Fig. 8, the display control function 154 displays an MPR image 540 corresponding to the extracted image 500 and the current image 410 side by side on the display 120. The current image 410 is the axial cross-sectional image 400 from which the extracted image 500 was extracted. In other words, the current image 410 shown in Fig. 8 is the same image as the axial cross-sectional image 400 shown in Fig. 4.

[0046] Note that MPR image 540 has the same cross section as current image 410, but the cross section of MPR image 540 may be a cross section different from that of current image 410, or may be any cross section that allows a wide range of second region 520 to be confirmed. For example, MPR image 540 may be a cross-cut image that is an image of a cross section closest to current image 410, as an arbitrary cross section that allows a wide range of second region 520 to be confirmed.

[0047] Fig. 9 is an enlarged view of the image 420 shown in Fig. 6. For example, as shown in Fig. 9, the display control function 154 displays an MPR image 540, a current image 410, and a previous image 420 side by side on the display 120. The previous image 420 is a medical image of the same subject captured in the past, and is an axial cross-sectional image 400 at the same position as the current image 410. For example, the previous image 420 is an image generated as an image at the cross-sectional position closest to that of the current image 410, using a warp field obtained by non-rigid registration.

[0048] Fig. 10 is an enlarged view of the image 430 shown in Fig. 6. For example, as shown in Fig. 10, the display control function 154 displays the MPR image 540, the current image 410, the past image 420, and the difference image 430 side by side on the display 120. The difference image 430 is an image that represents the difference between the current image 410 and the past image 420.

[0049] Fig. 11 is an enlarged view of the image 550 shown in Fig. 6. The display control function 154 causes the display 120 to display information that associates the area ratio between the first region 510 and the second region 520 of the extracted image 500 with the position of the spinal canal in the medical image.

[0050] For example, the display control function 154 performs rendering processing in the sagittal direction on a three-dimensional medical image composed of a plurality of axial cross-sectional images 400. For example, the display control function 154 performs SVR (Shaded Volume Rendering) processing as the rendering processing to generate an SVR image 550 shown in FIG. 11. Then, the display control function 154 assigns a color according to the index to a spinal canal region 551 in the SVR image 550 where the second regions 520 of the axial cross sections are gathered. In other words, the display control function 154 causes the display 120 to display a color image in which a color according to the index is assigned to each pixel of the spinal canal region 551 in the SVR image 550.

[0051] 11, a color corresponding to the area ratio value as an index is assigned by a color bar 553. Specifically, in the example shown in FIG. 11, when the area ratio is equal to or greater than the second threshold, the color image assigned to regions 551a and 551b of the spinal canal region 551 is shown in black on the drawing, but is instead shown in a color corresponding to the area ratio value, for example, blue. This allows the doctor to confirm spinal canal invasion by referring to the color corresponding to the index on the SVR image 550.

[0052] Fig. 12 is an enlarged view of the images 560 and 570 shown in Fig. 6. The display control function 154 causes the display 120 to display information that associates the area ratio between the first region 510 and the second region 520 of the extracted image 500 with the position of the spinal canal in the medical image.

[0053] For example, as shown in FIG. 12, the display control function 154 generates an SPR (Stretched Curved Planar Reconstruction) image 560 or a CPR (Curved MPR (Multi Planar Reconstruction)) of the spinal canal based on a three-dimensional medical image of the spinal canal. FIG. 12 shows an SPR image 560 in which the spinal canal is stretched in a straight line. Also, as shown in FIG. 12, the display control function 154 generates a graph 570 in which the horizontal axis indicates the area ratio between the first region 510 and the second region 520 of the extracted image 500 and the vertical axis indicates the position of the spinal canal on the SPR image 560. Then, the display control function 154 displays the SPR image 560 and the graph 570 side by side on the display 120. For example, if the color images assigned to areas 551a and 551b of the SVR image 550 shown in FIG. 11 are images that indicate the possibility of infiltration, the area ratio of the positions corresponding to the areas 551a and 551b of the spinal canal on the SPR image 560 in the graph 570 shown in FIG. 12 will be displayed as high.

[0054] In this way, the medical image processing apparatus 100 according to this embodiment derives an index relating to the degree of infiltration into the spinal canal based on the first region 510 in the spinal canal region where pixel values ​​are equal to or greater than the threshold value, and the second region 520 representing the spinal canal region, and displays the index superimposed on the medical image on the display 120. In this way, the medical image processing apparatus 100 according to this embodiment can assist in confirming infiltration into the spinal canal.

[0055] Note that the components of each device illustrated in the present embodiment are 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 them 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.

[0056] The method described in this embodiment can be realized by executing a prepared program on a computer such as a personal computer or a workstation. This program can be distributed via a network such as the Internet. This program can also be recorded on a non-transitory computer-readable recording medium such as a hard disk, flexible disk (FD), CD-ROM, MO, or DVD, and executed by being read from the recording medium by a computer.

[0057] According to at least one of the embodiments described above, confirmation of intraspinal canal invasion can be assisted.

[0058] Although several embodiments 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, modifications, and combinations of embodiments 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 and its equivalents as defined in the claims. [Explanation of symbols]

[0059] 1 Medical image processing system 100 Medical image processing device 152 Extraction function 153 Derived Functions 154 Display Control Function 510 1st area 520 Second area

Claims

1. an extraction unit that extracts a first region that is a region of the spinal canal included in the medical image; a derivation unit that derives an index relating to the degree of infiltration into the spinal canal based on an area ratio between the first region and a second region in the spinal canal where pixel values ​​are equal to or greater than a threshold; a display control unit that displays the indicator on a display unit; A medical image processing device comprising:

2. The derivation unit derives an area ratio between the first region and the second region as the index. The medical image processing device according to claim 1 .

3. The derivation unit further derives the index based on a spatial distribution of pixels that constitute the second region. The medical image processing device according to claim 2 .

4. the medical images are a plurality of cross-sectional images along a predetermined direction, the extraction unit extracts the first region from each of the plurality of cross-sectional images; The derivation unit derives the index based on continuity of the second region in the predetermined direction. The medical image processing device according to any one of claims 1 to 3.

5. the display control unit causes the display unit to display a color image in which a color corresponding to the value of the index is assigned to each pixel in the second region. The medical image processing device according to any one of claims 1 to 4.

6. the display control unit causes the display unit to display the determination result based on the index superimposed on the medical image. The medical image processing device according to any one of claims 1 to 5.

7. the display control unit causes the display unit to display information correlating the indices with the position of the spinal canal in the medical image. The medical image processing device according to any one of claims 1 to 6.

Images