Image processing program, image processing method, and image processing apparatus

Abstract

To provide image processing programs and the like that can generate image data and images that enable more adequate evaluation and diagnosis than conventional methods. [Solution] The present invention's image processing program is characterized by causing a computer to execute the following: an image acquisition process in step S305, which acquires a plurality of image data obtained by performing CT scanning of the hip joint of a subject; an image generation process in step S310, which generates display image data for displaying a predetermined image on a display device based on the plurality of image data acquired in the image acquisition process in step S305; and a first region identification process in step S320, which identifies a first region that is a necrotic area of ​​the femur surrounded by a plurality of image data corresponding to the femur, where the CT value is greater than that of a normal bone, based on the display image data of the femur in the hip joint generated in the image generation process in step S310.

Description

Technical Field

[0001] The present invention relates to image processing technology, and particularly to image processing technology for processing image data obtained by imaging with an imaging diagnostic apparatus such as a Computed Tomography (CT) apparatus or a Magnetic Resonance Imaging (MRI) apparatus for use in diagnosis.

Background Art

[0002] There are surgical treatment methods for various symptoms in a patient's joint, and preoperative diagnosis is important to smoothly proceed with the surgical procedure. For example, in the treatment of osteonecrosis of the femoral head, there is a surgical procedure called intertrochanteric varus osteotomy of the femur. This intertrochanteric varus osteotomy is a surgical procedure in which osteotomy is performed with bending at the trochanteric part of the femur, and the proximal bone fragment is rotated and moved in the varus direction to obtain a varus position, and the healthy area existing on the outside of the femoral head can be moved to the load-bearing part of the acetabulum to achieve symptom relief and the like. Conventionally, when performing this surgical procedure, first, a diagnosis is made based on simple anteroposterior X-ray images of both hip joints of the patient. In particular, using the maximum abduction anteroposterior X-ray image, it is confirmed whether the occupancy rate of the healthy part in the load-bearing part of the acetabulum after the operation is about 34% or more. And when it can be confirmed that the occupancy rate of the healthy part is about 34% or more, preoperative mapping is performed using a 2D template, and the movement distance of the proximal bone fragment in the surgical procedure is measured in advance. Thus, conventionally, using the simple X-ray image of the patient's hip joint, diagnosis regarding osteonecrosis of the femoral head and preoperative simulation of intertrochanteric varus osteotomy of the femur have been performed (for example, refer to Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the diagnosis using simple X-ray images of the hip joint, as described above, is limited to two-dimensional images, which is insufficient for diagnosing the three-dimensional shape of a patient's joint. Similarly, preoperative simulations are also limited to two-dimensional images of simple X-rays, which is insufficient for preoperative simulation of the three-dimensional shape of a patient's bone.

[0005] Therefore, an object of the present invention is to provide an image processing program, an image processing method, and an image processing apparatus that can generate image data and images that enable a more adequate evaluation and diagnosis than conventional methods. Another object of the present invention is to provide an image processing program, an image processing method, and an image processing apparatus that can generate image data and images that enable a more adequate preoperative simulation for evaluation than conventional methods. [Means for solving the problem]

[0006] The present invention has been made to solve at least some of the above-mentioned problems and can be realized in the following examples of applications. The reference numerals and supplementary explanations in parentheses in this section are provided to aid in understanding the present invention and indicate its correspondence with the embodiments described later; they do not limit the present invention in any way.

[0007] The image processing program for the first application example, which is one example of the application of the present invention, is characterized by causing a computer to execute the following steps: an image acquisition step (S305) to acquire a plurality of image data obtained by performing imaging of a predetermined joint (hip joint) of a subject with an image diagnostic device; an image generation step (S310) to generate display image data for displaying a predetermined image on a display device (display device 180) based on the plurality of image data acquired in the image acquisition step; and a first image identification step (S320) to identify a first region (first region 435) surrounded by a plurality of image data corresponding to the first bone portion (femur 400) of the predetermined joint, based on the display image data of the first bone portion generated in the image generation step, in which signal values ​​are greater than a predetermined value. The image processing program for the second application example, which is also one example of the application of the present invention, is an image processing program for the first application example, characterized in that in the first image identification step, the region containing a plurality of image data whose signal values ​​are consecutively greater than a predetermined value in a specific direction is identified as the first region.

[0008] An image processing program for a third application example, which is one of the application examples of the present invention, is an image processing program for a first application example, and its gist is to cause a computer to execute: a second image identification step (S330) which identifies a second region (second region 485) of the second bone (pelvic bone 450) at a predetermined joint based on the display image data relating to the coronal cross-section of the second bone (pelvic bone 450) at the predetermined joint generated in the image generation step; and a first image calculation step (S360) which calculates the area of ​​the region of the second region identified in the second image identification step that does not overlap with the first region in the axial direction of the subject, based on the display image data of the first bone and the second bone generated in the image generation step. Furthermore, the image processing program for the fourth application example, which is one of the application examples of the present invention, is the image processing program for the third application example, wherein the second bone is a pelvic bone, and in the second image identification step, in the displayed image data relating to the coronal cross-section of the second bone, a straight line connecting the left and right anterior superior iliac spine apexes of the second bone is defined as the first reference line (first reference line 460), and a straight line extending from the part of the acetabulum where the femoral head ligament attaches, which is parallel to the first reference line, is defined as the second reference line (second reference line 465), and the surface of the acetabulum located above the second reference line in the axial direction of the subject is identified as the second region.

[0009] The image processing program for the fifth application example, which is one of the application examples of the present invention, is an image processing program for the third application example, and its gist is to cause a computer to execute the following: a third image identification step (S520) which identifies a third region (third region 650) in the first bone, which is processed to be rotatable around the set coordinates, in an image of a predetermined region (inside the third reference line 600) centered on the set coordinates of the first bone, based on the display image data of the first bone and the second bone generated in the image generation step; and a second image calculation step (S560) which calculates the area of ​​a region that does not overlap with the first region and the subject in the axial direction of the subject when the third region identified in the third image identification step is rotated by a predetermined angle from the second region. Furthermore, the image processing program for the sixth application example, which is one of the application examples of the present invention, is the image processing program for the fifth application example, wherein the first bone is the femur, and in the third image identification step, the center of the femoral head portion when it is approximated as a sphere in the displayed image data of the first bone is set as the setting coordinate, and the gist of identifying the third region is a spherical portion that passes through the apex of the greater trochanter (greater trochanter 440) and the apex of the lesser trochanter (lesser trochanter 445) with the setting coordinate as the center.

[0010] The image processing method for the seventh application example, which is one of the application examples of the present invention, is characterized by performing an image acquisition step (S305) to acquire a plurality of image data obtained by performing imaging of a predetermined joint (hip joint) of a subject with an image diagnostic device; an image generation step (S310) to generate display image data for displaying a predetermined image on a display device (display device 180) based on the plurality of image data acquired in the image acquisition step; and a first image identification step (S320) to identify a first region (first region 435) surrounded by a plurality of image data corresponding to the first bone portion (femur 400) of the predetermined joint, based on the display image data of the first bone portion generated in the image generation step, in which the signal value is greater than a predetermined value.

[0011] An eighth application example of the present invention, which is one example of the application of the present invention, is an image processing method of the seventh application example, and is characterized by performing a second image identification step (S330) to identify a second region (second region 485) of the second bone (pelvic bone 450) in the predetermined joint based on the display image data relating to the coronal cross-section of the second bone (pelvic bone 450) in the predetermined joint, which is generated in the image generation step, and a first image calculation step (S360) to calculate the area of ​​the region of the second region identified in the second image identification step that does not overlap with the first region in the axial direction of the subject, based on the display image data of the first bone and the second bone generated in the image generation step.

[0012] An image processing method for the ninth application example, which is one of the application examples of the present invention, is an image processing method for the eighth application example, and is characterized by performing a third image identification step (S520) to identify a third region (third region 650) in the first bone, which is processed to be rotatable around the set coordinates, based on the display image data of the first bone generated in the image generation step, and a second image calculation step (S560) to calculate the area of ​​a region that does not overlap with the first region and the subject in the axial direction of the subject when the third region identified in the third image identification step is rotated by a predetermined angle from the second region.

[0013] The image processing apparatus for the 10th application example, which is one of the application examples of the present invention, comprises: an image acquisition unit (image acquisition unit 105) that acquires a plurality of image data obtained by performing imaging of a predetermined joint (hip joint) of a subject with an image diagnostic device; an image generation unit (image generation unit 110) that generates display image data for displaying a predetermined image on a display device (display device 180) based on the plurality of image data acquired by the image acquisition unit; and a first image identification unit (first image identification unit 122) that identifies a first region (first region 435) surrounded by a plurality of image data corresponding to the first bone portion (femur 400) of the predetermined joint, based on the display image data of the first bone portion generated by the image generation unit, in which the signal value is greater than a predetermined value.

[0014] The image processing apparatus for the 11th application example, which is one of the application examples of the present invention, is an image processing apparatus for the 10th application example, comprising: a second image identification unit (second image identification unit 124) that identifies a second region (second region 485) of the second bone portion (pelvic bone 450) at a predetermined joint based on the display image data relating to the coronal cross-section of the second bone portion (pelvic bone 450) at the predetermined joint, generated by the image generation unit; and a first image calculation unit (image calculation unit 140) that calculates the area of ​​a region of the second region identified by the second image identification unit that does not overlap with the first region in the axial direction of the subject, based on the display image data of the first bone portion and the second bone portion generated by the image generation unit.

[0015] The image processing apparatus for the 12th application example, which is one of the application examples of the present invention, is an image processing apparatus for the 11th application example, comprising: a third image identification unit (third image identification unit 126) that identifies a third region in the first bone, which is processed to be rotatable around the set coordinates, based on the display image data of the first bone generated by the image generation unit, and a second image calculation unit (image calculation unit 140) that calculates the area of ​​a region that does not overlap with the first region and the subject in the axial direction of the subject when the third region identified by the third image identification unit is rotated by a predetermined angle from the second region, based on the display image data of the first bone and the second bone generated by the image generation unit. [Brief explanation of the drawing]

[0016] [Figure 1] This is an overall configuration diagram of an image processing apparatus according to one embodiment of the present invention. [Figure 2] This flowchart shows the overall process performed by the image processing device. [Figure 3] This is a flowchart for preoperative image processing. [Figure 4-1] This is an explanatory diagram illustrating the image generation process in preoperative image processing. [Figure 4-2] This is an explanatory diagram illustrating the process of identifying the first region in preoperative image processing. [Figure 4-3] This is an explanatory diagram illustrating the process of identifying the second region in preoperative image processing. [Figure 4-4] This is an explanatory diagram illustrating various preoperative diagnostic images based on various processed diagnostic image data generated during the diagnostic image processing in preoperative image processing. [Figure 5] This is a flowchart showing the preoperative simulation process. [Figure 6-1] This is an explanatory diagram illustrating the process of identifying the third domain in preoperative simulation processing. [Figure 6-2]It is an explanatory diagram illustrating an image for surgical simulation generated by the image processing for simulation in preoperative simulation processing. [Figure 6-3] It is an explanatory diagram illustrating various images for simulation generated by the image processing for simulation in preoperative simulation processing.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, embodiments to which the present invention is applied will be described with reference to the drawings. Note that the embodiments of the present invention are not limited to the following embodiments, and various forms can be adopted as long as they belong to the technical scope of the present invention.

[0018] [1. Configuration of Image Processing Apparatus 100] First, the configuration of the image processing apparatus 100 will be described using FIG. 1. FIG. 1 is an overall configuration diagram of an image processing apparatus 100 according to one embodiment of the present invention. The image processing apparatus 100 is constituted by, for example, a general-purpose computer system, and each component or function in the image processing apparatus 100 described below is realized by executing a computer program stored in a computer-readable recording medium or the like.

[0019] As shown in FIG. 1, the image processing apparatus 100 includes an image acquisition unit 105, an image generation unit 110, an image identification unit 120, a display image processing unit 130, and an image calculation unit 140. Further, an external storage device that is an image diagnostic device and stores a plurality of image data composed of a plurality of CT images obtained by imaging (CT scan) with a CT (Computed Tomography) device (not shown), an input device 170 that receives various input operations to the image processing apparatus 100, and a display device 180 that displays various information output from the image processing apparatus 100 are connected to the image processing apparatus 100. The plurality of image data stored in the external storage device are a plurality of 3D voxel data based on a plurality of slice images (axial images) obtained by CT scanning of a subject.

[0020] The image processing device 100 is a device that performs image processing, such as generating images for diagnosing symptoms in a specific joint of a subject, based on multiple image data. The image acquisition unit 105 acquires multiple image data relating to a specific joint from an external storage device based on information input by the input device 170, and transmits the acquired multiple image data to the image generation unit 110. Based on the multiple image data, the image generation unit 110 generates various display image data for displaying various images of the subject on the display device 180, such as MPR (Multi-Planar Reformation) images, CPR (Curved Planar Reformation) images, MIP (Maximum Intensity Projection) images, and 3D images using the VR (Volume Rendering) method. The image identification unit 120 comprises a first image identification unit 122, a second image identification unit 124, and a third image identification unit 126. Based on various display image data generated by the image generation unit 110, the first image identification unit 122 identifies a first region of the first bone, the second image identification unit 124 identifies a second region of the second bone, and the third image identification unit 126 identifies a third region of the first bone. The display image processing unit 130 applies various processing operations, such as masking, to the various display image data generated by the image generation unit 110 to generate various processed image data for displaying an image on the display device 180 that shows a predetermined region identified by the image identification unit 120. The image calculation unit 140 calculates various numerical values, such as the area of ​​a predetermined region identified by the image identification unit 120, based on the various display image data generated by the image generation unit 110 and the various processed image data generated by the display image processing unit 130.

[0021] [2. Explanation of the overall process] Next, the overall processing performed by the image processing device 100 will be described using Figure 2. Figure 2 is a flowchart showing the overall processing performed by the image processing device 100. In this embodiment, the image processing program, image processing method, and image processing device can perform processing based on CT images of various joints, such as the hip joint and shoulder joint, as predetermined joints. However, in the following, the image processing is for performing a diagnosis of femoral head necrosis and a preoperative simulation of intertrochanteric varus osteotomy, and the predetermined joint is the hip joint, the first bone part is the femur, and the second bone part is the pelvic bone.

[0022] As shown in Figure 2, when the overall process is executed, first the image processing device 100 performs preoperative image processing (S300) to generate preoperative diagnostic images for use in diagnosing femoral head necrosis. Next, the image processing device 100 determines whether or not to perform the preoperative simulation processing described later based on the input from the input device 170 (input operation by the operator) (S400). If it determines not to perform the preoperative simulation processing (NO in S400), the overall process is terminated. On the other hand, if the image processing device 100 determines to perform preoperative simulation processing to generate preoperative simulation images for use in preoperative simulation of intertrochanteric varus osteotomy (YES in S400), it performs preoperative simulation processing (S500). In other words, by performing this overall process, it is possible to generate only preoperative diagnostic images for the purpose of performing only a diagnosis, and it is also possible to generate preoperative simulation images for the purpose of performing a preoperative simulation after performing a diagnosis using the preoperative diagnostic images.

[0023] [3. Explanation of preoperative image processing] Next, using Figure 3, we will describe the preoperative image processing in step S300 of the overall processing performed by the image processing device 100. Figure 3 is a flowchart of the preoperative image processing.

[0024] As shown in Figure 3, when preoperative image processing is performed, first, the image acquisition unit 105 performs an image acquisition process (S305) to acquire multiple image data of the vicinity of the hip joint from an external storage device based on input from the input device 170 (input operation by the operator). Next, the image generation unit 110 performs an image generation process (S310) to generate various display image data of the hip joint, femur, and pelvic bone based on the multiple image data of the vicinity of the hip joint acquired in the image acquisition process of step S305. Next, the first image identification unit 122 performs a first region identification process (S320) to identify a first region corresponding to the necrotic portion of the femoral head of the femur based on the display image data generated in the image generation process of step S310. Next, the second image identification unit 124 performs a second region identification process (S330) to identify a second region corresponding to the load-bearing surface of the acetabulum of the pelvic bone based on the display image data generated in the image generation process of step S310. Next, the display image processing unit 130 performs a diagnostic image processing (S350) to generate diagnostic processed image data for displaying a preoperative diagnostic image representing the first and second regions on the display device 180, based on the display image data generated in the image generation process of step S310, the first region identified by the first region identification process of step S320, and the second region identified by the second region identification process of step S330. Next, the image calculation unit 140 calculates the area of ​​the first region based on the first region identified by the first region identification process of step S320, calculates the area of ​​the second region based on the second region identified by the second region identification process of step S330, calculates the area of ​​a specific region which is the region where the first and second regions overlap in the axial direction of the subject's body, and performs a healthy region occupancy rate calculation process (S360) to calculate the healthy region occupancy rate based on these areas. Then, based on the input from the input device 170 (input operation by the operator), a decision is made (S370) as to whether or not to terminate the preoperative image processing. If it is decided to terminate the preoperative image processing (YES in S370), the preoperative image processing is terminated. On the other hand, if it is decided not to terminate the preoperative image processing (NO in S370), the system waits until the preoperative image processing is terminated.In step S370, the input from the input device 170 refers, for example, to a signal from the input device 170 based on the operator's selection operation regarding an image displayed on the display device 180 prompting the operator to choose whether or not to terminate preoperative image processing.

[0025] [4-1. Explanation of Image Generation Process] Next, using Figure 4-1, the image generation process in step S310 of preoperative image processing will be explained. Figure 4-1 is an explanatory diagram illustrating the image generation process in preoperative image processing, where (A) is a diagram showing an example of image data stored in an external storage device, (B) is a diagram showing an example of an MPR image generated by the image generation unit 110, and (C) is a diagram showing an example of a three-dimensional image generated by the image generation unit 110.

[0026] As shown in Figure 4-1, the image data stored in the external storage device consists of multiple 3D voxel data based on volume data of slice images (axial images) obtained by CT scans (see Figure 4-1(A)). When the image acquisition process in step S305 is executed, as described above, the image acquisition unit 105 acquires multiple image data relating to the femur and pelvic bones constituting the hip joint from the external storage device based on the input from the input device 170. Then, when the image generation process in step S310 is executed, as described above, the image generation unit 110 generates display image data constituting multiple coronal images, which are MPR images of the hip joint as shown in Figure 4-1(B), and display image data constituting a 3D image of the hip joint using the VR method, as shown in Figure 4-1(C), based on the image data acquired by the image acquisition unit 105. Here, the various display image data generated by the image generation unit 110 in the image generation process are displayed on the display device 180 in response to the input from the input device 170 (input operation by the operator).

[0027] [4-2. Explanation of the first domain identification process] Next, using Figure 4-2, the first region identification process in step S320 of preoperative image processing will be explained. Figure 4-2 is an explanatory diagram illustrating the first region identification process in preoperative image processing, where (A) is an explanatory diagram illustrating the relationship between the CT value and the position of multiple image data (voxels) in the femur of femoral head necrosis, and (B) is an explanatory diagram illustrating the first region in the coronal section image (coronal image) of the hip joint (femur). In the various processes of this embodiment described below, since the image data of this embodiment is obtained by CT scanning, the signal values ​​in the image data will be explained as CT values.

[0028] As shown in Figure 4-2, in the first region identification process, the sclerotic margin of the femoral head is extracted based on the display image data of multiple coronal images generated by the image generation process in step S310, and the first region, which is the necrotic area of ​​the femoral head, is identified. Here, it is known that in femurs with necrotic areas, a sclerotic margin exists, and the image data (voxels) at the positions corresponding to the sclerotic margin are larger than the CT values ​​of normal bone (for example, the CT values ​​of normal cancellous bone are +150HU to +500HU). Therefore, as shown in Figure 4-2(A), the relationship between the CT values ​​of multiple image data (voxels) in the femur and their positions is such that, in multiple image data (voxels) that are continuous in a specific direction from the normal bone on the intertrochanteric side toward the femoral head with the sclerotic margin and necrotic area, the image data (voxels) at the positions corresponding to normal bone are within the range of the CT values ​​of normal bone, but the image data (voxels) at the positions corresponding to the portion leading to the sclerotic margin are larger than the CT values ​​of normal bone. Furthermore, as shown in Figure 4-2(A), when the width of the hardened edge is large (when there are many image data (voxels) aligned in the specific direction), the CT values ​​of multiple image data (voxels) that are continuous in the specific direction and located at positions corresponding to the hardened edge may gradually increase. Therefore, when the first region identification process is executed, the first image identification unit 122 can identify the image data (voxels) that are continuously larger than a predetermined value (for example, +150HU to +500HU), which is the CT value of a normal bone area, among the multiple image data (voxels) that are continuous in the specific direction, as the end of the hardened edge. Then, after identifying multiple specific directions, the hardened edge in the multiple image data (voxels) that are continuous in each specific direction can be identified as the first region by the region enclosed by the multiple image data (voxels) that are the hardened edge of the femoral head.The above-mentioned CT values ​​and predetermined values ​​for normal bone areas are merely examples. The CT values ​​for normal bone areas vary depending on the condition of the subject's bones, the subject's age, the tube voltage of the imaging device, and the area considered to be normal bone. Therefore, various values ​​that can be set according to the operator's input are acceptable. For example, if the area near the medulla of the femur is considered normal bone, the CT value for normal bone may be less than +150 HU. In this case, the CT value for normal bone (predetermined value) may be set to a value less than +150 HU.

[0029] As shown in Figure 4-2(B), specifically, for example, based on a coronal image of the vicinity of the hip joint consisting of the femur 400 and pelvic bone 450, a plurality of continuous image data (voxels) are identified along the direction toward the femoral head (direction of the arrow shown in Figure 4-2(B)) on a perpendicular line to the line connecting the greater trochanter and lesser trochanter of the femur 400. The CT value for each position of the plurality of image data (voxels) is calculated, and the image data (voxel) with a CT value greater than a predetermined value and closest to the intertrochanteric line is identified. The region containing a plurality of image data (voxels) located toward the femoral head than the identified image data (voxel) is identified as the image data (voxels) located in the first region 430 of the displayed image data of the coronal image. By performing this process for each of the plurality of specific directions, the first region 430 of the displayed image data of the coronal image can be identified. Then, the first image identification unit 122 can identify a first region 435 (see Figure 4-4(A)), which is the necrotic portion of the femoral head in the femur 400 of the three-dimensional image described later, based on a plurality of first region sections 430 identified by performing a first region identification process on the display image data of a plurality of coronal cross-sectional images.

[0030] [4-3. Explanation of the second domain identification process] Next, using Figure 4-3, the second region identification process in step S330 of preoperative image processing will be explained. Figure 4-3 is an explanatory diagram illustrating the second region identification process in preoperative image processing, where (A) is an explanatory diagram illustrating the first and second reference lines in the coronal section image of the pelvic bone, (B) is an explanatory diagram illustrating the relationship between the second reference line and the second region in the coronal section image of the pelvic bone, (C) is an explanatory diagram illustrating the second region in the three-dimensional image of the pelvic bone, and (D) is an explanatory diagram illustrating the second region in the three-dimensional image of the pelvic bone.

[0031] As shown in Figure 4-3, in the second region identification process, the second region, which is the load-bearing surface in the acetabulum of the pelvic bone, is identified based on the display image data of multiple coronal cross-sectional images (coronatal images) generated by the image generation process in step S310. First, as shown in Figure 4-3(A), when the second region identification process is executed, the second image identification unit 124, based on the input from the input device 170 (input operation by the operator), identifies a straight line connecting the left and right anterior superior iliac spine apex of the pelvic bone 450 as the first reference line 460 in the display image data of the coronal cross-sectional image (coronatal image) near the hip joint consisting of the femur 400 and the pelvic bone 450, and identifies a straight line extending from the point 470 where the ligament of the femoral head attaches in the acetabulum, which is parallel to the first reference line 460, as the second reference line 465. Specifically, for example, based on the coronal image of the vicinity of the hip joint displayed on the display device 180, the operator determines the positions of the left and right anterior superior iliac spine peaks on the pelvic bone 450 and the position of the attachment point 470 of the femoral head ligament in the acetabulum. Then, the second image identification unit 124 identifies the second reference line 465 together with the first reference line 460. Next, as shown in Figure 4-3(B), the second image identification unit 124 identifies the surface of the acetabulum located above the second reference line (towards the head) in the axial direction of the subject as the second region 480 in the displayed image data of the coronal image of the vicinity of the hip joint, which consists of the femur 400 and the pelvic bone 450. Then, the second image identification unit 124 can identify a second region 485, which is the load-bearing surface in the acetabulum of the pelvic bone 450 in the three-dimensional image, based on the multiple second region sections 480 identified by performing a second region identification process on the display image data of multiple coronal cross-sectional images, as shown in Figures 4-3(C) and (D).

[0032] [4-4. Explanation of diagnostic image processing] Next, using Figure 4-4, the diagnostic image processing in step S350 of preoperative image processing will be explained. Figure 4-4 is an explanatory diagram illustrating various preoperative diagnostic images based on various diagnostic processed image data generated in the diagnostic image processing. (A) is an explanatory diagram illustrating the first diagnostic image showing the first region in a three-dimensional image of the femur. (B) is an explanatory diagram illustrating the second diagnostic image showing the first region, second region, and specific region in a three-dimensional image of the femur. (C) is an explanatory diagram illustrating the third diagnostic image showing the second region and specific region in a three-dimensional image of the femur.

[0033] As shown in Figure 4-4, in the diagnostic image processing, diagnostic processed image data for various three-dimensional preoperative diagnostic images is generated based on the display image data of the three-dimensional image generated by the image generation process in step S310, the first region 435 in the femoral head of the femur 400 identified by the first region identification process in step S320, and the second region 485 in the acetabulum of the pelvic bone 450 identified by the second region identification process in step S330. First, as shown in Figure 4-4(A), when the diagnostic image processing is executed, the display image processing unit 130 matches the first region 435 along the body axis direction of the subject to the display image data of the three-dimensional image of the femur 400, thereby generating first diagnostic processed image data of a first diagnostic image in which the first region 435 is represented in the femoral head of the three-dimensional image of the femur 400. Next, as shown in Figure 4-4(B), the display image processing unit 130 matches the second region 485 along the body axis of the subject to the first diagnostic processed image data of the first diagnostic image, thereby generating second diagnostic image processed data of the second diagnostic image in which the first region 435 and the second region 485 are represented on the femoral head in the three-dimensional image of the femur 400. Here, in the second diagnostic image processed data of the second diagnostic image, the region where the first region 435 and the second region 485 overlap (superimpose) is identified as a specific region 437 corresponding to the necrotic region of the femoral head located within the load-bearing surface of the acetabulum before intertrochanteric varus osteotomy. Then, as shown in Figure 4-4(C), the display image processing unit 130 generates a third diagnostic image based on the second diagnostic processed image data of the second diagnostic image, in which the second region 485 and the specific region 437 are represented on the femoral head in the three-dimensional image of the femur 400. In the above description, the first diagnostic image, the second diagnostic image, and the third diagnostic image are various preoperative diagnostic images, and the first diagnostic processed image data, the second diagnostic processed image data, and the third diagnostic processed image data are various diagnostic processed image data.

[0034] [4-5. Explanation of the calculation process for the healthy area occupancy rate] Here, we will explain the healthy area occupancy rate calculation process in step S360 of the preoperative image processing. In the healthy area occupancy rate calculation process, based on the display image data generated by the image generation process in step S310, the healthy area occupancy rate is calculated, which is the occupancy rate of the healthy area on the acetabular load-bearing surface where the necrotic area of ​​the femoral head is not located within the acetabular load-bearing surface of the acetabulum. In other words, this healthy area occupancy rate calculation process in preoperative image processing makes it possible to calculate the healthy area occupancy rate before intertrochanteric varus osteotomy. First, when the healthy area occupancy rate calculation process is executed, the image calculation unit 140 calculates the area (number of voxels) of the first region 435 identified by the first region identification process and the area (number of voxels) of the second region 485 identified by the second region identification process, based on the display image data generated by the image generation process, and then calculates the area (number of voxels) of the identified region 437. Next, the image calculation unit 140 calculates (area of ​​second region 485) - (area of ​​specific region 437) and defines the healthy area as the healthy area occupancy rate. The image calculation unit 140 calculates (area of ​​healthy area occupancy) / (area of ​​second region 485) and defines the healthy area occupancy rate as the healthy area occupancy rate. The display device 180 then displays various preoperative diagnostic images (see Figure 4-4) along with the area of ​​the first region 435 (number of voxels), the area of ​​the second region 485 (number of voxels), the area of ​​the specific region 437 (number of voxels), the healthy area occupancy rate, and the healthy area occupancy rate, all calculated in the healthy area occupancy rate calculation process. For example, in the diagnostic processed image data shown in Figure 4-4, the area of ​​the second region 485 is "4195" and the area occupied by the healthy tissue is "2888". Therefore, the healthy tissue occupancy rate before intertrochanteric varus osteotomy is "68.8%", and these values ​​are displayed on the display device 180 along with the various preoperative diagnostic images. When various values ​​are displayed on the display device 180 along with various preoperative diagnostic images in this way, various values ​​such as the healthy tissue occupancy rate can be recognized, allowing for a more thorough evaluation and diagnosis than before.Furthermore, when the image calculation unit 140 calculates the area (number of voxels) of the first region 435, the second region 485, and the specific region 437, it performs erosion of morphological image processing on each region to shrink one voxel and perform the calculation. In addition, the image calculation unit 140 may calculate the specific region 437 based on various diagnostic processed image data that constitute various preoperative diagnostic images generated by diagnostic image processing.

[0035] [5. Explanation of preoperative simulation processing] Next, using Figure 5, we will describe the pre-operative simulation process in step S500 of the overall process performed by the image processing device 100. Figure 5 is a flowchart of the pre-operative simulation process.

[0036] As shown in Figure 5, when the preoperative simulation process is executed, first, the third image identification unit 126 performs a third region identification process (S520) to identify a third region corresponding to the osteotomy site of the femur in intertrochanteric varus osteotomy, based on the display image data generated in the image generation process of step S310. Next, the display image processing unit 130 performs a simulation image processing process (S550) to generate display image data that constitutes a preoperative simulation image to be displayed on the display device 180 for use in the preoperative simulation, based on the display image data generated in the image generation process of step S310 and the third region identified by the third region identification process of step S520. Next, the image calculation unit 140 calculates the area of ​​a specific region based on the area of ​​the first region and the area of ​​the second region calculated in the healthy region occupancy rate calculation process in step S360 of the preoperative image processing, and the display image data of the preoperative simulation image generated by the simulation image processing in step S550, and performs a healthy region occupancy rate calculation process (S560) to calculate the healthy region occupancy rate based on these areas. Next, it determines whether there is a predetermined input (a predetermined input operation by the operator) from the input device 170 to the preoperative simulation image generated by the simulation image processing in step S550 (S565). If it determines that there is a predetermined input (a predetermined input operation by the operator) from the input device 170 (YES in S565), it executes the simulation image processing in step S550 to generate display image data of the preoperative simulation image corresponding to the predetermined input from the input device 170, and then executes the healthy region occupancy rate calculation process in step S560.Then, in step S565, if there is no predetermined input from the input device 170 (predetermined input operation by the operator) (YES in S565), a decision is made (S570) whether or not to terminate the preoperative simulation process based on the input from the input device 170 (input operation by the operator). If it is decided to terminate the preoperative simulation process (YES in S570), the preoperative simulation process is terminated. On the other hand, if it is decided not to terminate the preoperative simulation process (NO in S570), a decision is made in step S565 whether or not there is a predetermined input from the input device 170 (predetermined input operation by the operator). Note that the input from the input device 170 in the decision in step S570 refers to, for example, a signal from the input device 170 based on the operator's selection operation in response to an image displayed on the display device 180 prompting the operator to make a selection regarding whether or not to terminate the preoperative simulation process, and is different from the predetermined input from the input device 170 in the decision in step S565, which will be described later.

[0037] [6-1. Explanation of the third domain identification process] Next, using Figure 6-1, we will explain the third region identification process in step S520 of the preoperative simulation process. Figure 6-1 is an explanatory diagram illustrating the third region identification process in the preoperative simulation process, and in particular, it is an explanatory diagram illustrating the third reference line in the coronal section image (coronal image) near the right hip joint.

[0038] As shown in Figure 6-1, in the third region identification process, the third region corresponding to the osteotomy site of the femur 400 in intertrochanteric varus osteotomy is identified based on the display image data of multiple coronal images generated by the image generation process in step S310. First, when the third region identification process is executed, the third image identification unit 126 approximates the femoral head of the femur 400 as a sphere and identifies the set coordinates that will be the center of the sphere. In the display image data of the coronal images near the hip joint consisting of the femur 400 and the pelvic bone 450, a circle that forms a spherical portion passing through the apex of the greater trochanter 440 and the apex of the lesser trochanter 445 of the femur 400 with the set coordinates as the center (a circle passing through the apex of the greater trochanter 440 and the apex of the lesser trochanter 445 with the set coordinates as the center) is identified as the third reference line 600. Specifically, for example, when the operator identifies the outer edge of the femoral head of the femur 400 based on at least two display image data from among slice images (axial image), coronal image, and sagittal image of the femoral head of the femur 400, the third image identification unit 126 identifies the center of the outer edge as a set coordinate. Next, when the operator determines the positions of the greater trochanter 440 and lesser trochanter 445 on the femur 400 based on the display image data of a coronal image of the vicinity of the hip joint, which consists of the femur 400 and the pelvic bone 450, the third image identification unit 126 identifies a third reference line 600 centered on the identified set coordinate. Then, the third image identification unit 126 identifies the femoral head located inside the third reference line as the third region 650 in the display image data of the coronal image. Furthermore, the third image identification unit 126 can identify a third region corresponding to the osteotomy site of the femur 400 in a three-dimensional image of the femur 400 in a femoral intertrochanteric varus osteotomy, based on the multiple third region regions 650 identified by performing a third region identification process on the display image data of multiple coronal cross-sectional images (coronatal images). In addition, in the above-described third region identification process, the third image identification unit 126 may identify the third reference line 600 based, for example, on the setting of the position of the set coordinates by the operator and the length of the radius of the third reference line 600.

[0039] [6-2. Explanation of image processing for simulation] Next, using Figures 6-2 and 6-3, the simulation image processing in step S550 of the preoperative simulation process will be explained. Figure 6-2 is an explanatory diagram illustrating the surgical procedure simulation image generated by the simulation image processing. Figure 6-3 is an explanatory diagram illustrating various simulation images generated by the simulation image processing, where (A) is an explanatory diagram illustrating the first simulation image showing the first region in the 3D image of the femur, (B) is an explanatory diagram illustrating the second simulation image showing the first region, second region, and a specific region in the 3D image of the femur, and (C) is an explanatory diagram illustrating the third simulation image showing the second region and a specific region in the 3D image of the femur.

[0040] As shown in Figure 6-2, first, in the simulation image processing, the display image data of the coronal section image (coronatal image) generated by the image generation process in step S310 and the third region portion 650 of the femoral head of the femur 400 identified by the third region identification process in step S520 are used to generate processed image data for the surgical simulation image. When the simulation image processing is executed, the display image processing unit 130 generates processed image data for the surgical simulation image that displays the third reference line 600 identified by the third region identification process and the third region portion 650 of the femur 400, which can rotate at a predetermined angle around the center coordinates of the third reference line 600 in accordance with a predetermined input from the input device 170 (a predetermined input operation by the operator), on the display image data of the coronal section image (coronatal image) near the hip joint consisting of the femur 400 and the pelvic bone 450. Then, as shown in Figure 6-2, when a predetermined input (a predetermined input operation by the operator) is received from the input device 170 (YES in S565), the display image processing unit 130 generates processed image data for the surgical simulation image, which shows the femur 400 in a state where the third region has been rotated by a predetermined angle corresponding to the predetermined input. In other words, when the surgical simulation image is displayed on the display device 180, if the operator receives an input via the input device 170 to rotate the third region 650 by a predetermined angle, the third region 650 rotated by that predetermined angle can be displayed.

[0041] As shown in Figure 6-3, in the next simulation image processing, various simulation processing image data for various 3D simulation images are generated based on the 3D image generated by the image generation process in step S310, the first region 435 in the femoral head of the femur 400 identified by the first region identification process in step S320, the second region 485 in the acetabulum of the pelvic bone 450 identified by the second region identification process in step S330, and the surgical simulation processing image data of the surgical simulation image described above. First, as shown in Figure 6-3(A), the display image processing unit 130 matches the first region 435 along the body axis direction of the subject to the 3D image of the femur 400 in which the third region has been rotated by a predetermined angle, thereby generating the first simulation processing image data for the first simulation image in which the first region 435 is represented on the femoral head in the 3D image of the femur 400. Next, as shown in Figure 6-3(B), the display image processing unit 130 matches the second region 485 along the body axis direction of the subject to the first simulation processing image data of the first simulation image, thereby generating a second simulation processing image data of the second simulation image in which the first region 435 and the second region 485 are represented on the femoral head in the three-dimensional image of the femur 400. Here, in the second simulation processing image data of the second simulation image, the region where the first region 435 and the second region 485 overlap (superimpose) is identified as a postoperative specific region 637 corresponding to the necrotic region of the femoral head located within the load-bearing surface of the acetabulum after intertrochanteric varus osteotomy. Then, as shown in Figure 6-3(C), the display image processing unit 130 generates a third simulation image based on the second simulation processing image data of the second simulation image, in which the second region 485 and the postoperative specific region 637 are represented on the femoral head in the three-dimensional image of the femur 400.In the above, the first simulation image, the second simulation image, and the third simulation image are various simulation images, and the first simulation processing image data, the second simulation processing image data, and the third simulation processing image data are various simulation processing image data.

[0042] [6-3. Explanation of the calculation process for the percentage of healthy tissue occupancy] Here, we will explain the healthy area occupancy rate calculation process in step S560 of the preoperative simulation processing. In the healthy area occupancy rate calculation process, the healthy area occupancy rate is calculated based on the area (number of voxels) of the first region 435 and the area (number of voxels) of the second region 485 calculated in the healthy area occupancy rate calculation process in step S360, and the area (number of voxels) of the postoperative specific region 637 calculated based on various simulation processing image data generated by the simulation image processing process in step S550, similar to the healthy area occupancy rate calculation process in the preoperative image processing described above. In other words, this healthy area occupancy rate calculation process in the preoperative simulation processing makes it possible to calculate the healthy area occupancy rate after the third region has been rotated by a predetermined angle in response to a predetermined input to the image for surgical procedure simulation, and after the femoral head has been rotated in a so-called intertrochanteric varus osteotomy. At this time, the image calculation unit 140 also calculates a predetermined angle value obtained by rotating the third region 650, and calculates the healthy area occupancy rate according to the predetermined angle value. Then, the display device 180 displays the area (number of voxels) of the first region 435 and the area (number of voxels) of the second region 485 calculated in the healthy area occupancy rate calculation process in the preoperative image processing, along with the predetermined angle calculated in the healthy area occupancy rate calculation process in the preoperative simulation processing, the area (number of voxels) of the postoperative specific region 637, the healthy area occupancy area, and the healthy area occupancy rate. In other words, when the display device 180 is displaying an image for surgical procedure simulation or various simulation images, if the operator inputs via the input device 170 to rotate the third region 650 by a predetermined angle, the numerical value of the predetermined angle, the area of ​​the postoperative specific region 637 corresponding to the predetermined angle, the area occupied by the healthy area, and the healthy area occupancy rate will be displayed on the display device 180.For example, in the various simulation processing image data shown in Figure 6-3, the predetermined angle is "10°", the area of ​​the second region 485 is "4195", and the healthy area occupied is "3754". Therefore, the healthy area occupancy rate after intertrochanteric varus femoral osteotomy is "89.5%", and these values ​​are displayed on the display device 180 along with the surgical simulation image or various simulation images. When various values ​​are displayed on the display device 180 along with the surgical simulation image in this way, the operator can recognize various values ​​such as the healthy area occupancy rate when the predetermined angle is set to a desired value in response to the operation of the input device 170, so that preoperative simulations that are closer to actual intertrochanteric varus femoral osteotomy can be performed than before. Furthermore, when the surgical simulation image is displayed on the display device 180, if the operator inputs to rotate the third region 650 via the input device 170, the color of the numerical value of the healthy area occupancy rate corresponding to the predetermined angle rotated of the third region 650 may be changed or the numerical value may be made to blink if it exceeds a threshold (for example, 34%).

[0043] [Description of the features of this embodiment] The image processing program of the above embodiment is characterized by causing a computer to execute the following: an image acquisition process in step S305, which acquires a plurality of image data obtained by performing CT scanning of the hip joint of a subject; an image generation process in step S310, which generates display image data for displaying a predetermined image on a display device 180 based on the plurality of image data acquired in the image acquisition process in step S305; and a first region identification process in step S320, which identifies a first region 435 surrounded by a plurality of image data corresponding to the femur 400, where the CT value is greater than that of a normal bone, based on the display image data of the femur 400 in the hip joint generated in the image generation process in step S310.

[0044] With such an image processing program, for example, a first region 430 can be identified in a single coronal image corresponding to the femur 400, and then diagnostic image data can be generated that identifies the first region 435, which is the necrotic area in the femur 400 in a 3D image, based on multiple first region 430s identified by multiple coronal images. Therefore, with such an image processing program, diagnosis can be made using preoperative diagnostic images that clearly show the necrotic area equivalent to the actual necrotic area in the femur in a 3D image, thus enabling a more thorough evaluation and diagnosis compared to conventional 2D image-based diagnosis. Furthermore, with such an image processing program, since the first region 435 in the 3D image of the femur 400 is based on multiple first region 430s identified by multiple coronal images, the processed diagnostic image data can identify not only the surface but also the necrotic area inside the femur 400, enabling a more thorough evaluation and diagnosis compared to conventional 2D image-based diagnosis.

[0045] Furthermore, in the image processing program of the above embodiment, the first region identification process in step S320 is characterized in that, in the display image data of the femur 400, the region containing multiple image data whose CT values ​​are consecutively greater than the CT values ​​of normal bone is identified as the first region 435. With such an image processing program, in the display image data of the femur 400, among multiple image data whose CT values ​​are consecutively greater than the CT values ​​of normal bone, the image data on the starting point side (intertrochanteric line side) of the specific direction is identified as the end of the sclerotic edge, and the first region 435 containing multiple image data whose CT values ​​are zero or less than the CT values ​​of normal bone is identified as the necrotic area. As a result, it is possible to make a diagnosis using a preoperative diagnostic image that clearly shows the necrotic area in the femur that is close to the actual symptoms of the subject, and a more adequate evaluation diagnosis is possible compared to conventional two-dimensional image-based diagnosis.

[0046] Furthermore, the image processing program of the above embodiment is characterized by causing the computer to execute the following: a second region identification process in step S330, which identifies a second region 485 that is the load-bearing surface of the acetabulum in the pelvic bone 450, based on the display image data relating to the coronal cross-section of the pelvic bone 450 in the hip joint generated in the image generation process of step S310; and a healthy area occupancy rate calculation process in step S360, which calculates the area of ​​the region in the second region 485 identified in the second region identification process of step S330 that does not overlap with the first region 435 in the axial direction of the subject, based on the display image data of the femur 400 and the pelvic bone 450 generated in the image generation process of step S310.

[0047] According to such an image processing program, for example, a second region 480 can be identified in a single coronal image corresponding to the pelvic bone 450, and then diagnostic image data can be generated that identifies the second region 485, which is the load-bearing surface of the acetabulum in the 3D image of the pelvic bone 450, based on multiple second region regions 480 identified by multiple coronal images. Therefore, with such an image processing program, it is possible to perform a diagnosis using a preoperative diagnostic image in which a load-bearing surface equivalent to the actual load-bearing surface of the acetabulum in the pelvic bone is clearly defined in a 3D image, thus enabling a more thorough evaluation and diagnosis compared to conventional 2D image-based diagnosis. Furthermore, this image processing program calculates the area of ​​the second region 485, which is the load-bearing surface of the pelvic bone 450, in a 3D image of the hip joint, that does not overlap with the first region 435, which is the necrotic portion of the femur 400, in the axial direction of the subject's body. By doing so, it is possible to calculate a healthy tissue occupancy rate equivalent to that of the actual subject's healthy tissue occupancy rate. Therefore, a more accurate diagnosis can be made compared to conventional 2D image-based diagnosis.

[0048] Furthermore, in the image processing program of the above embodiment, in the second region identification process of step S330, in the display image data relating to the coronal section of the pelvic bone 450, a straight line connecting the left and right anterior superior iliac spine apex of the pelvic bone 450 is defined as the first reference line 460, a straight line extending from the portion 470 where the ligament of the femoral head attaches in the acetabulum and parallel to the first reference line 460 is defined as the second reference line 470, and the surface of the acetabulum located above the second reference line 470 in the axial direction of the subject is identified as the second region 485. With such an image processing program, in the display image data of the three-dimensional pelvic bone 450, diagnostic image data is generated in which the second region 485 in the acetabulum of the pelvic bone 450 is identified as the load-bearing surface. Therefore, diagnosis can be performed using preoperative diagnostic images that clearly show a load-bearing surface equivalent to the actual load-bearing surface in the acetabulum of the pelvic bone, enabling a more thorough evaluation compared to conventional 2D image-based diagnosis.

[0049] Furthermore, the image processing program of the above embodiment is characterized by causing the computer to execute the following: a third region identification process in step S520, which identifies a third region 650 in the femur 400 that is processed to be rotatable around the set coordinates, and is an image inside a third reference line 600 centered on the set coordinates of the femur 400, based on the display image data of the femur 400 generated in the image generation process of step S310; and a healthy area occupancy rate calculation process in step S560, which calculates the area of ​​the region that does not overlap with the first region 435 in the direction of the subject's body axis when the third region 650 identified in the third region identification process of step S520 is rotated by a predetermined angle from the second region 485.

[0050] Such an image processing program can, for example, identify a third region 650 in the femur 400 and generate image data for surgical simulation that shows the third region 650 in the femur 400, processed to be rotatable around a set coordinate. Therefore, with such an image processing program, preoperative simulations can be performed using images for surgical simulation that allow the operator to rotate the third region corresponding to the osteotomy site in the femur at a desired angle. This makes it possible to perform preoperative simulations with a more adequate evaluation compared to conventional preoperative simulations using 2D images. Furthermore, with this image processing program, in the 3D image of the hip joint after rotating the third region 650 at a desired angle in the processed image for surgical simulation, the area of ​​the region that does not overlap with the first region 435, which is the necrotic portion of the femur 400 in the axial direction of the subject, within the second region 485, which is the load-bearing surface of the pelvic bone 450, can be calculated. Since a healthy area occupancy rate equivalent to that of the actual subject can be calculated, it is possible to perform a preoperative simulation with sufficient evaluation compared to conventional preoperative simulations using 2D images.

[0051] Furthermore, in the image processing program of the above embodiment, the third region identification process in step S520 is characterized in that, in the display image data of the femur, the center when the femoral head portion is approximated as a sphere is set as the set coordinate, and a spherical portion passing through the apex of the greater trochanter 440 and the apex of the lesser trochanter 445 with the set coordinate as the center is identified as the third region 650. With such an image processing program, in the display image data of the 3D femur 400 which is the processed image data for simulation, the third region 650 in the greater trochanter 400 can be identified as the osteotomy site. This makes it possible to clearly identify the osteotomy site in the femur equivalent to that when an actual intertrochanteric varus osteotomy is performed, and to perform a preoperative simulation with sufficient evaluation compared to conventional preoperative simulations using 2D images.

[0052] The image processing method of the above embodiment is characterized by performing the following steps: an image acquisition process in step S305 to acquire multiple image data obtained by performing CT imaging of the hip joint of a subject; an image generation process in step S310 to generate display image data for displaying a predetermined image on a display device 180 based on the multiple image data acquired in the image acquisition process in step S305; and a first region identification process in step S320 to identify a first region 435 surrounded by multiple image data corresponding to the femur 400, where the CT value is greater than that of a normal bone, based on the display image data of the femur 400 in the hip joint generated in the image generation process in step S310.

[0053] With this image processing method, for example, a first region 430 can be identified in a single coronal image corresponding to the femur 400, and then diagnostic image data can be generated that identifies the first region 435, which is the necrotic area in the femur 400 in a three-dimensional image, based on multiple first region 430s identified by multiple coronal images. Therefore, with this image processing method, it is possible to make a diagnosis using a preoperative diagnostic image that clearly shows the necrotic area in a three-dimensional image that is equivalent to the necrotic area in the actual femur, thus enabling a more thorough evaluation and diagnosis compared to conventional two-dimensional image-based diagnosis. Furthermore, with this image processing method, since the first region 435 in the three-dimensional image of the femur 400 is based on multiple first region 430s identified by multiple coronal images, the processed diagnostic image data can identify not only the surface but also the necrotic area inside the femur 400, enabling a more thorough evaluation and diagnosis compared to conventional two-dimensional image-based diagnosis.

[0054] Furthermore, the image processing method of the above embodiment is characterized by performing a second region identification process in step S330, which identifies a second region 485 that is the load-bearing surface of the acetabulum in the pelvic bone 450, based on display image data relating to the coronal cross-section of the pelvic bone 450 in the hip joint, generated in the image generation process of step S310, and a healthy area occupancy rate calculation process in step S360, which calculates the area of ​​the region in the second region 485 identified in the second region identification process of step S330 that does not overlap with the first region 435 in the axial direction of the subject, based on display image data of the femur 400 and the pelvic bone 450 generated in the image generation process of step S310.

[0055] With this image processing method, for example, a second region 480 can be identified in a single coronal image corresponding to the pelvic bone 450, and then diagnostic image data can be generated that identifies the second region 485, which is the load-bearing surface of the acetabulum in the 3D image of the pelvic bone 450, based on multiple second region regions 480 identified by multiple coronal images. Therefore, with this image processing method, it is possible to perform a diagnosis using a preoperative diagnostic image in which a load-bearing surface equivalent to the actual load-bearing surface of the acetabulum in the pelvic bone is clearly defined in a 3D image, thus enabling a more thorough evaluation and diagnosis compared to conventional 2D image-based diagnosis. Furthermore, this image processing method allows for the calculation of the area of ​​the second region 485, which is the load-bearing surface of the pelvic bone 450, in the hip joint of a 3D image, that does not overlap with the first region 435, which is the necrotic portion of the femur 400, in the axial direction of the subject's body. By doing so, a healthy area occupancy rate equivalent to that of the actual subject's healthy area occupancy rate can be calculated, enabling a more accurate assessment and diagnosis compared to conventional 2D image-based diagnosis.

[0056] Furthermore, the image processing method of the above embodiment is characterized by performing a third region identification process in step S520, which identifies a third region 650 in the femur 400 that is an image inside a third reference line 600 centered on the set coordinates of the femur 400 and is processed to be rotatable around the set coordinates, based on the display image data of the femur 400 generated in the image generation process of step S310, and a healthy area occupancy rate calculation process in step S560, which calculates the area of ​​the region that does not overlap with the first region 435 in the direction of the subject's body axis when the third region 650 identified in the third region identification process of step S520 is rotated by a predetermined angle from the second region 485.

[0057] With this image processing method, for example, the third region 650 in the femur 400 can be identified, and then image data for surgical simulation can be generated showing the third region 650 in the femur 400, which has been processed to be rotatable around a set coordinate. Therefore, with such an image processing program, preoperative simulations can be performed using images for surgical simulation that allow the operator to rotate the third region corresponding to the osteotomy site in the femur at a desired angle. This makes it possible to perform preoperative simulations with a more adequate evaluation compared to conventional 2D image-based diagnosis. Furthermore, with this image processing method, in the 3D image of the hip joint after rotating the third region 650 at a desired angle in the processed image for surgical simulation, the area of ​​the region in the second region 485, which is the load-bearing surface of the pelvic bone 450, that does not overlap with the first region 435, which is the necrotic portion of the femur 400, in the axial direction of the subject's body can be calculated. This makes it possible to calculate a healthy area occupancy rate equivalent to that of the actual subject's healthy area occupancy rate. This makes it possible to perform preoperative simulations with a more adequate evaluation compared to conventional 2D image-based preoperative simulations.

[0058] The image processing apparatus of the above embodiment is characterized by comprising: an image acquisition unit 105 that acquires a plurality of image data obtained by performing CT imaging of the hip joint of a subject; an image generation unit 110 that generates display image data for displaying a predetermined image on a display device 180 based on the plurality of image data acquired by the image acquisition unit 105; and a first image identification unit 122 that identifies a first region 435 surrounded by a plurality of image data corresponding to the femur 400, with a CT value greater than a predetermined value, based on the display image data of the femur 400 in the hip joint generated by the image generation unit 110.

[0059] With such an image processing device, for example, a first region 430 can be identified in a single coronal image corresponding to the femur 400, and then diagnostic image data can be generated that identifies the first region 435, which is the necrotic portion in the femur 400 in a three-dimensional image, based on multiple first region 430s identified by multiple coronal images. Therefore, with such an image processing device, it is possible to perform a diagnosis using a preoperative diagnostic image that clearly shows the necrotic portion in a three-dimensional image that is equivalent to the necrotic portion in the actual femur, thus enabling a more thorough evaluation and diagnosis compared to conventional two-dimensional image-based diagnosis. Furthermore, with such an image processing device, since the first region 435 in the three-dimensional image of the femur 400 is based on multiple first region 430s identified by multiple coronal images, the processed diagnostic image data can identify not only the surface but also the necrotic portion inside the femur 400, enabling a more thorough evaluation and diagnosis compared to conventional two-dimensional image-based diagnosis.

[0060] Furthermore, the image processing apparatus of the above embodiment is characterized by comprising: a second image identification unit 124 that identifies a second region 485 of the pelvic bone 450 based on display image data relating to the coronal cross-section of the pelvic bone 450 in the hip joint generated by the image generation unit 110; and an image calculation unit 140 that calculates the area of ​​the region of the second region 485 identified by the second image identification unit 124 that does not overlap with the first region 435 in the axial direction of the subject, based on display image data of the femur 400 and the pelvic bone 450 generated by the image generation unit 110.

[0061] With such an image processing device, for example, a second region 480 can be identified in a single coronal image corresponding to the pelvic bone 450, and then diagnostic image data can be generated that identifies the second region 485, which is the load-bearing surface of the acetabulum in the 3D image of the pelvic bone 450, based on multiple second region regions 480 identified by multiple coronal images. Therefore, with such an image processing device, it is possible to perform a diagnosis using a preoperative diagnostic image in which a load-bearing surface equivalent to the actual load-bearing surface of the acetabulum in the pelvic bone is clearly defined in a 3D image, thus enabling a more thorough evaluation and diagnosis compared to conventional 2D image-based diagnosis. Furthermore, with this image processing device, in a 3D image of the hip joint, the area of ​​the second region 485, which is the load-bearing surface of the pelvic bone 450, that does not overlap with the first region 435, which is the necrotic portion of the femur 400, in the axial direction of the subject's body can be calculated. Based on this, a healthy area occupancy rate equivalent to that of the actual subject's healthy area occupancy rate can be calculated, thus enabling a more adequate evaluation and diagnosis compared to conventional 2D image-based diagnosis.

[0062] Furthermore, the image processing apparatus of the above embodiment is characterized by comprising: a third image identification unit 126 that identifies a third region 650 in the femur 400, which is an image inside a third reference line 600 centered on the set coordinates of the femur 400 and is processed to be rotatable around the set coordinates, based on display image data of the femur 400 and the pelvic bone 450 generated by the image generation unit 110; and an image calculation unit 140 that calculates the area of ​​a region that does not overlap with the first region 435 in the direction of the subject's body axis when the third region 650 identified by the third image identification unit 126 is rotated by a predetermined angle from the second region 485.

[0063] With such an image processing device, for example, a third region 650 in the femur 400 can be identified, and then processed image data for surgical simulation can be generated showing the third region 650 in the femur 400, which is processed to be rotatable around a set coordinate. Therefore, with such an image processing program, preoperative simulations can be performed using images for surgical simulation that allow the operator to rotate the third region corresponding to the osteotomy site in the femur at a desired angle. This makes it possible to perform preoperative simulations with a more adequate evaluation compared to conventional preoperative simulations using two-dimensional images. Furthermore, with such an image processing device, in the 3D image of the hip joint after rotating the third region 650 at a desired angle in the processed image for surgical procedure simulation, the area of ​​the region in the second region 485, which is the load-bearing surface of the pelvic bone 450, that does not overlap with the first region 435, which is the necrotic portion of the femur 400, in the axial direction of the subject's body can be calculated. Since a healthy area occupancy rate equivalent to that of the actual subject's healthy area occupancy rate can be calculated, it is possible to perform a preoperative simulation with sufficient evaluation compared to conventional preoperative simulations using 2D images.

[0064] [Other embodiments] In the above-described embodiment, the diagnostic imaging device was identified as a CT scanner, but it may be any type of diagnostic imaging device, and more specifically, it may be an MRI (Magnetic Resonance Imaging) scanner. In other words, in the image processing of the above-described embodiment, various processing may be performed based on multiple image data obtained by imaging with an MRI scanner (MRI examination), or various processing may be performed based on multiple image data obtained by a CT scan and multiple image data obtained by an MRI examination. Specifically, based on multiple image data obtained by a CT scan of a predetermined joint and multiple image data obtained by an MRI examination, various three-dimensional display image data representing the cartilage of the predetermined joint and the organs surrounding the predetermined joint may be generated, and then various preoperative diagnostic images, surgical simulation images, and simulation images that can also represent the cartilage of the predetermined joint may be generated. Furthermore, in the first region identification processing of step S320, when processing based on multiple image data obtained by an MRI examination is performed, the signal values ​​of the image data may be used to identify the first region. With such image processing, it is possible to generate image data and images that enable more adequate evaluation in diagnosis and preoperative simulation compared to conventional methods.

[0065] In the embodiments described above, image processing for diagnosing femoral head necrosis in the hip joint and for performing preoperative simulation of intertrochanteric varus osteotomy of the femoral bone was explained. However, the image processing may also be used for diagnosing or simulating other diseases. For example, the image processing described above may be used for diagnosing acetabular dysplasia in the hip joint and for performing preoperative simulation of rotational acetabular osteotomy. Specifically, when the image processing described above is used for diagnosing acetabular dysplasia, in the preoperative image processing, the first region may not be identified in the first region identification processing, but the second region identified in the second region identification processing may be identified, and the diagnosis may be made based on the area of ​​the second region corresponding to the load-bearing surface. In the preoperative simulation processing, the third region of the pelvic bone may be identified in the third region identification processing, and simulation processing image data may be generated in the simulation image processing processing, with the third region being the osteotomy site for rotational acetabular osteotomy. With this type of image processing, it is possible to generate image data and images that enable more adequate evaluation and preoperative simulation compared to conventional methods, for the diagnosis of acetabular dysplasia in the hip joint and for preoperative simulation of acetabular rotational osteotomy. Furthermore, the preoperative image processing in the above-mentioned image processing may also be used to perform diagnosis of avascular necrosis of the humeral head in the shoulder joint. Specifically, when using the preoperative image processing in the above-mentioned image processing for the diagnosis of avascular necrosis of the humeral head, diagnostic processed image data may be generated based on a first region corresponding to the necrotic portion of the humerus identified by the first region identification processing, and a second region corresponding to the load-bearing surface of the scapular glenoid operculum identified by the second region identification processing. With this type of image processing, it is possible to generate image data and images that enable more adequate evaluation and diagnosis compared to conventional methods, for the diagnosis of avascular necrosis of the humeral head.

[0066] In the above-described embodiment, as shown in Figure 4-2, in the first region identification process of step S320, among a plurality of image data (voxels) that are continuous in a specific direction, the image data (voxel) closest to the intertrochanteric line, which has a CT value that is continuously greater than a predetermined value which is the CT value of a normal bone area, is identified as the end of the hardening edge. However, for example, the end of the hardening edge may be identified based on the slope value of the CT value with respect to a plurality of image data (voxels) that are continuous in a specific direction. Specifically, in the image processing, the first image identification unit 122 may identify the image data (voxel) closest to the intertrochanteric line, which has a CT value slope value that is continuously greater than zero, among a plurality of image data (voxels) that are continuous in a specific direction, as the end of the hardening edge. With such image processing, it is possible to generate image data and images that enable more adequate evaluation in diagnosis and preoperative simulation compared to conventional methods.

[0067] In the above-described embodiment, as shown in Figure 4-3, in the second region identification process of step S330, the second image identification unit 124 identifies the first reference line 460 and the second reference line 465 based on the operator's setting of the positions of the left and right anterior superior iliac spine apexes of the pelvic bone 450 and the attachment position 470 of the femoral head ligament of the acetabulum. However, for example, the second image identification unit may identify the first reference line, etc., in a predetermined manner without the operator performing various position determination operations. Specifically, in image processing, the second image identification unit may be made capable of estimating the positions of the anterior superior iliac spine apex and the attachment position of the femoral head ligament of the acetabulum based on the results of image processing based on image data of multiple subjects, and when the second region identification process is executed, the second image identification unit may identify the first reference line and the second reference line based on the estimated positions. With such image processing, it is possible to generate image data and images that enable more adequate evaluation in diagnosis and preoperative simulation compared to conventional methods. In this type of image processing, the position of the anterior superior iliac spine apex estimated by the second image identification unit and the attachment position of the femoral head ligament of the acetabulum may be appropriately changed by the operator through the operation of an input device.

[0068] In the above-described embodiment, as shown in Figure 6-1, in the third region identification process of step S520, the third image identification unit 126 identifies the third reference line 600 based on the operator's setting of the outer edge of the femoral head of the femur 400, the position of the greater trochanter 440, and the position of the lesser trochanter 445. However, for example, the third image identification unit may identify the third reference line in a predetermined manner without the operator having to perform various position determination operations. Specifically, in the image processing, the third image identification unit may be made capable of estimating the outer edge of the femoral head, the position of the greater trochanter, and the position of the lesser trochanter based on the results of image processing based on image data of multiple subjects, and when the third region identification process is executed, the third reference line may be identified based on the positions estimated by the third image identification unit. In such image processing, the positions of the greater trochanter and lesser trochanter of the femur estimated by the third image identification unit may be made appropriately change by the operator's operation of the input device. With this type of image processing, it is possible to generate image data and images that enable preoperative simulations with a much better evaluation than conventional methods.

[0069] In the above-described embodiment, as shown in Figure 6-2, the image for surgical procedure simulation was based on a coronal section image, but it may also be based on a three-dimensional image, for example. Specifically, in the simulation image processing, the processing image data for the three-dimensional surgical procedure simulation image may be generated based on the display image data of the three-dimensional image generated by the image generation process and the third region identified by the third region identification process. Furthermore, in the three-dimensional surgical procedure simulation image generated in this way, when it is displayed on a display device, the operator may be able to display the third region rotated horizontally and vertically with respect to the body axis direction of the subject by operating through an input device. Furthermore, when the surgical simulation image is displayed on the display device 180, if the operator inputs via the input device 170 to rotate the third region 650 by a predetermined angle in the horizontal and vertical directions relative to the subject's body axis, the display device may be configured to show the numerical value of the predetermined angle in the horizontal direction, the numerical value of the predetermined angle in the vertical direction, the area of ​​the postoperative specific region corresponding to the predetermined angle, the area occupied by the healthy area, and the healthy area occupancy rate. With such image processing, it is possible to generate image data and images that enable preoperative simulation with a more adequate evaluation than conventional methods.

[0070] The present invention has been described above based on embodiments and modifications. However, the embodiments of the invention described above are for the purpose of facilitating understanding of the present invention and do not limit it. The present invention can be modified and improved without departing from its spirit and claims, and the present invention includes equivalents thereof. [Explanation of Symbols]

[0071] 100...Image processing device, 105...Image acquisition unit, 110...Image generation unit, 120...Image identification unit, 122...First image identification unit, 124...Second image identification unit, 126...Third image identification unit, 130...Display image processing unit, 140...Image calculation unit, 170...Input device, 180...Display device.

Claims

1. An image acquisition step involves obtaining multiple image data obtained by performing imaging on a specific joint of the subject using an imaging diagnostic device, An image generation step, based on the plurality of image data acquired in the image acquisition step, generates display image data for displaying a predetermined image on a display device, A first image identification step, based on the display image data of the first bone portion at the predetermined joint generated in the image generation step, identifies a first region surrounded by a plurality of image data corresponding to the first bone portion, where the signal value is greater than a predetermined value. An image processing program that causes a computer to perform an image processing operation.

2. An image processing program according to claim 1, An image processing program that, in the first image identification step, identifies a region in the display image data of the first bone portion that includes a plurality of image data whose signal values ​​are consecutive in a specific direction and whose signal values ​​are consecutively greater than a predetermined value, as the first region.

3. An image processing program according to claim 1, A second image identification step involves identifying a second region of the second bone based on the display image data relating to the coronal cross-section of the second bone at the predetermined joint, generated in the image generation step. A first image calculation step, based on the display image data of the first bone portion and the second bone portion generated in the image generation step, calculates the area of ​​the region of the second region identified in the second image identification step that does not overlap with the first region in the axial direction of the subject, An image processing program that causes a computer to perform an image processing operation.

4. The image processing program according to claim 3, The second bone mentioned above is the pelvic bone, In the second image identification step, an image processing program identifies the second region as follows: in the displayed image data relating to the coronal section of the second bone, a straight line connecting the left and right anterior superior iliac spine apexes of the second bone is defined as the first reference line, a straight line extending from the portion of the acetabulum where the femoral head ligament attaches and parallel to the first reference line is defined as the second reference line, and the surface of the acetabulum located above the second reference line in the axial direction of the subject.

5. The image processing program according to claim 3, A third image identification step, based on the display image data of the first bone generated in the image generation step, identifies a third region of the first bone that is processed to be rotatable around the set coordinates, in an image of a predetermined region centered on the set coordinates of the first bone. A second image calculation step calculates the area of ​​the region that does not overlap with the first region and the region of the subject in the axial direction of the body when the third region identified in the third image identification step is rotated by a predetermined angle from the second region, based on the display image data of the first bone region and the second bone region generated in the image generation step; An image processing program that causes a computer to perform an image processing operation.

6. The image processing program according to claim 5, The first bone is the femur, The third image identification step involves an image processing program that, in the displayed image data of the first bone portion, sets the center of the spherical approximation of the femoral head portion as the set coordinate, and identifies the third region as a spherical portion that passes through the apex of the greater trochanter and the apex of the lesser trochanter with the set coordinate as the center.

7. An image acquisition step involves obtaining multiple image data obtained by performing imaging on a specific joint of the subject using an imaging diagnostic device, and An image generation step, based on the plurality of image data acquired in the image acquisition step, generates display image data for displaying a predetermined image on a display device, A first image identification step, based on the display image data of the first bone portion at the predetermined joint generated in the image generation step, identifies a first region surrounded by a plurality of image data corresponding to the first bone portion, where the signal value is greater than a predetermined value. An image processing method that performs this task.

8. The image processing method according to claim 7, A second image identification step involves identifying a second region of the second bone based on the display image data relating to the coronal cross-section of the second bone at the predetermined joint, generated in the image generation step. A first image calculation step calculates the area of ​​the region of the second region identified in the second image identification step that does not overlap with the first region in the axial direction of the subject, based on the display image data of the first bone region and the second bone region generated in the image generation step, An image processing method that performs this task.

9. The image processing method according to claim 8, A third image identification step, based on the display image data of the first bone generated in the image generation step, identifies a third region of the first bone that is processed to be rotatable around the set coordinates, in an image of a predetermined region centered on the set coordinates of the first bone. A second image calculation step calculates the area of ​​the region that does not overlap with the first region and the region in the axial direction of the subject when the third region identified in the third image identification step is rotated by a predetermined angle from the second region, based on the display image data of the first bone region and the second bone region generated in the image generation step, An image processing method that performs this task.

10. An image acquisition unit that acquires multiple image data obtained by performing imaging of a specific joint of the subject using an imaging diagnostic device, An image generation unit generates display image data for displaying a predetermined image on a display device based on the plurality of image data acquired by the image acquisition unit, A first image identification unit identifies a first region surrounded by a plurality of image data corresponding to the first bone in a predetermined joint, based on the display image data of the first bone in the predetermined joint generated by the image generation unit, wherein the signal value of the plurality of image data is greater than a predetermined value. An image processing device equipped with the following features.

11. An image processing apparatus according to claim 10, A second image identification unit identifies a second region of the second bone based on the display image data relating to the coronal cross-section of the second bone at the predetermined joint, generated by the image generation unit, A first image calculation unit calculates the area of ​​the region of the second region identified by the second image identification unit that does not overlap with the first region in the axial direction of the subject, based on the display image data of the first bone region and the second bone region generated by the image generation unit, An image processing device equipped with the following features.

12. An image processing apparatus according to claim 11, A third image identification unit identifies a third region of the first bone, which is processed to be rotatable around the set coordinates, based on the display image data of the first bone generated by the image generation unit, and is an image of a predetermined region centered on the set coordinates of the first bone. A second image calculation unit calculates the area of ​​the region that does not overlap with the first region in the axial direction of the subject when the third region identified by the third image identification unit is rotated by a predetermined angle from the second region, based on the display image data of the first bone region and the second bone region generated by the image generation unit, An image processing device equipped with the following features.

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