JP2011217797A - Image processing apparatus and method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce manual processing when extracting a specific region from a medical image.SOLUTION: This image processing apparatus includes a region extracting part and an image processing algorithm execution part. The region extracting part includes: a time information acquiring means for acquiring time information related to the imaging time of the medical image from input volume data; an imaging time phase estimating means for estimating the imaging time phase of the medical image based on the acquired time information; a region extraction processing classification determining means for determining region extraction processing classification including a region extraction algorithm for extracting a region for image processing from the medical image, and a parameter required to carry out the region extraction algorithm according to the estimated imaging time phase; and a region extraction processing execution means for executing region extraction processing to the input volume data by the region extraction processing of the determined classification. The image processing algorithm execution part executes a specific image processing algorithm to the volume data of the extracted region.

Description

  The present invention relates to an image processing apparatus and method for extracting a specific region from a three-dimensional medical image.

  Three-dimensional medical images (hereinafter referred to as `` medical image volume data '', `` volume '') taken using medical image inspection equipment such as X-ray computed tomography (CT) equipment and magnetic resonance imaging (MRI) equipment Visualization processing is performed on a plurality of medical image processing algorithms to assist diagnosis. In the medical image processing algorithm used here, there are multiple types of data depending on the case, such as extracting and displaying only the necessary part data from the input volume data or displaying the image with its edge highlighted. Processing exists.

  Also, in recent years, due to technological advances in medical imaging devices, the number of tomographic images (hereinafter also referred to as “slice images”) that can be captured at a time (while the gantry of the CT device rotates once) has increased at an accelerated rate. The amount of volume data output as a result of shooting is also enormous. When displaying images using a medical image processing algorithm, it is very difficult to automatically apply all the processing in consideration of safety and accuracy. Modification is mandatory. However, the more manual checks and corrections, the higher the burden and human cost on doctors and engineers. These costs are even higher when the volume of volume data is enormous. In order to reduce such a burden, it is required to increase the accuracy and validity of the part of the processing that is automatically performed as much as possible.

  In medical image processing, there is a technique for extracting a specific organ or disease region from a medical image using a contrast agent. In the case of imaging using a contrast agent, the target region is repeatedly imaged at a plurality of timings after injecting the contrast agent. The imaging timing defined by the length of time from the contrast agent injection time to the imaging start time is called the imaging time phase. For example, in a CT image in which a liver tumor is imaged using a contrast agent, the contrast agent is injected. The image in the arterial phase taken after several tens of seconds is taken at the timing when the contrast agent penetrates the artery best, and in this arterial phase the tumor part is stained more strongly by the contrast agent than the surrounding liver parenchyma ( However, it is known that the tumor part shows a lower CT value than the surrounding liver parenchyma because the contrast medium penetrates the entire liver parenchyma in images taken later than that in the vein phase. It has been.

  For the purpose of improving the extraction accuracy of the region compared to the case of using a one-time phase image by utilizing the change of the pixel value (CT value in the CT image) that appears due to this property, that is, the difference in imaging timing, Region extraction methods using temporal medical images have been proposed (Patent Document 1, Non-Patent Documents 1 and 2). For example, in Patent Document 1, the first time phase and the second time phase image series having different shooting time phases are used, the CT value of the first time phase image data is set to the X axis, and the CT value of the second time phase is set to Y. In the two-dimensional feature space along the axis, a two-dimensional histogram is obtained for each pixel at the same position in both time phase spaces, and the CT value range of the pixel corresponding to the liver region is estimated based on the two-dimensional histogram. Thus, a method for automating the extraction of a liver region from a CT image using a contrast agent has been proposed. Further, Non-Patent Document 1 uses dynamic concentration images at the same time, using an early phase image and a late phase image in which a tumor is clearly contrasted, and using the concentration information at the same time. We have proposed a method to prevent many regions from being extracted. Non-Patent Document 2 proposes a technique for integrating information of 4-time multi-slice CT images, extracting a liver region, and highlighting a tumor portion.

Japanese Patent No. 3486615

Yuki Wakita et al .: “Detection of liver cancer based on measurement of concentration characteristics between time phases of multi-phase abdominal X-ray CT images” Journal of Computer Aided Imaging Society, Vol.10, No.1, pp1-10, Mar 2007 Junya Nakagawa et al .: “Development of an automatic extraction method for liver tumors from multi-dimensional 3D multi-slice CT images” IEICE Transactions, J87-D-II, pp260-270, 2004

  However, in these methods, it is necessary to grasp in advance in what time phase the target data was taken. However, since the target volume data itself does not include information on the time phase at which the image was taken, the pre-operation to manually set the information on the time phase that was taken before image processing was started. Necessary. Therefore, an image processing method, an image processing apparatus, and a program that reduce manual processing when extracting a specific region from a three-dimensional medical image are required.

The disclosed image processing apparatus and method thereof are as follows. An image processing apparatus that applies a specific image processing algorithm to input volume data of a medical image, and includes time information acquisition means for acquiring time information related to the imaging time of the medical image from the input volume data, and time information acquisition means Based on the time information acquired by the above, the imaging time phase estimation means for estimating the imaging time phase of the medical image, and the area to be image-processed from the medical image according to the imaging time phase estimated by the imaging time phase estimation means The region extraction algorithm for extracting the region, the region extraction processing type determination means for determining the region extraction processing type including the parameters necessary for executing the region extraction algorithm, and the region extraction processing of the type determined by the region extraction processing type determination means A region extraction unit having region extraction processing execution means for performing region extraction processing on volume data; The volume data of the extraction region by the extraction process execution means, provided an image processing algorithm execution unit for executing a specific image processing algorithms.

  According to the present invention, when a specific region is extracted from a medical image, manual processing can be reduced.

It is a block diagram of the image processing apparatus of this embodiment. It is a flowchart of the automatic area | region extraction process in an automatic area | region extraction part. It is an image of medical image data stored in the DICOM format. It is an imaging time phase calculation table A showing an example of an imaging time phase calculation table. It is an image figure of the medical image volume data which image | photographed the abdomen of the patient who has liver cancer, and shows the difference in the image in two time phases of the late arterial phase and the venous phase. It is an extraction process type determination table A showing an example of an extraction process type determination table. It is a photographing time phase calculation table B showing an example of a photographing time phase calculation table. It is an extraction process type determination table B showing an example of an extraction process type determination table. It is the imaging | photography time phase calculation table C which shows an example when an imaging | photography timing and automatic extraction likelihood are defined. It is an extraction processing type determination table B showing an example when the shooting timing is included in the shooting time phase. It is a physique index calculation table and an extraction parameter correction formula.

Hereinafter, an image processing apparatus, an image processing method, and a program according to an embodiment of the present invention will be described in detail with reference to the drawings.
FIG. 1 is a configuration diagram of an image processing apparatus according to the present embodiment.
As shown in FIG. 1, an image processing apparatus 11 that performs image processing on volume data captured by an X-ray CT apparatus, an MRI apparatus, or the like, an external storage apparatus 10 that stores volume data, and instructions for starting each process Input device 12 and display device 13 for displaying the image processed image.

  The image processing apparatus 11 includes an internal memory 20, an automatic area extraction unit 21, and an image processing algorithm execution unit 22. The automatic area extraction unit 21 includes a time information acquisition unit 30 that acquires time information from volume data, a shooting time phase estimation unit 31 that estimates a shooting time phase based on the acquired time information, and a time phase that is estimated. A region extraction processing type determination unit 32 that determines a region extraction processing type (region extraction processing algorithm and its parameters) and a region extraction processing execution unit 33 that executes region extraction processing of the determined type are provided. The automatic area extraction unit 21 and the image processing algorithm execution unit 22 are realized by program processing or a dedicated circuit by a CPU (Central Processing Unit) provided in the image processing apparatus 11. Further, a storage unit (not shown) for storing programs for realizing the functions of the internal memory 20 and the image processing device 21 includes a RAM (Random Access Memory), a ROM (Read Only Memory), and an HDD (Hard Disk Drive). And a storage medium such as a flash memory.

The process until the automatic area extraction result is displayed on the display device 13 will be described.
First, the image processing apparatus 11 receives an input of an instruction to read volume data from the input device 12, and transfers the volume data stored in the external storage device 10 to the internal memory 20. Next, the time information acquisition unit 30 acquires time information from the volume data stored in the internal memory 20 in accordance with an instruction to start the automatic region extraction process from the input device 12, and the acquired time information is obtained as a shooting time phase estimation unit. Forward to 31. The shooting time phase estimation unit 31 estimates the shooting time phase based on the transferred time information, and transfers the estimated shooting time phase to the region extraction processing type determination unit 32. The region extraction process type determination unit 32 determines the region extraction process type based on the estimated shooting time phase. The region extraction processing execution unit 33 performs automatic region extraction processing on the volume data stored in the internal memory 20 by the region extraction processing of the determined type, and sends the automatic region extraction processing result to the image processing algorithm execution unit 22. Forward. The image processing algorithm execution unit 22 executes image processing corresponding to the extracted area, and displays the image processing result on the display device 13.

  FIG. 2 is a flowchart of region extraction processing in the automatic region extraction unit 11. First, the volume data stored in the external storage device 10 is input to the internal memory 20 in the image processing device 11 (step S201). The time information acquisition unit 30 acquires the contrast agent injection time and the imaging time as time information from the volume data stored in the internal memory 20 (step S202). It will be described later that time information is included in the volume data. The imaging time phase estimation unit 31 estimates the time phase at which each tomographic image is acquired based on the acquired contrast agent injection time and imaging time (step S203). The region extraction processing type determination unit 32 determines a region extraction algorithm, an appropriate parameter value, and the like corresponding to the estimated time phase of each tomographic image as the region extraction processing type (step S204). The region extraction processing execution unit 33 performs region extraction processing on the input volume data by the region extraction processing of the determined type (step S205), and transfers the result of the region extraction processing to the image processing algorithm execution unit 22. The image processing algorithm execution unit 22 executes predetermined image processing on the image data of the extracted area, and displays the image processing result on the display device 13 (step S206).

Acquisition of time information from the volume data by the time information acquisition unit 30 and estimation of the time phase by the shooting time phase estimation unit 31 will be described.
As an example of information attached to a medical image, a standard called DICOM (Digital Imaging and Communication in Medicine) will be described. The DICOM standard was standardized by ACR-NEMA (American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA)), and 1993 RSNA (Radiological Society of North America) And is currently widely used as a standard for communication between systems in the radiation department. Specifically, it defines the format of a medical image taken by an X-ray CT apparatus, an MRI apparatus, etc., and a communication protocol between medical image devices that handle these images. In many cases, medical images taken by a medical imaging apparatus are stored in a format according to the DICOM standard, and a plurality of tags called DICOM headers are added to raw image information represented by CT values and the like. FIG. 3 shows an image of medical image data stored in the DICOM format.

  In this embodiment, time information is acquired from the tag of the DICOM header. The time information in the present embodiment refers to the contrast agent injection start time and the imaging start time. In the DICOM tag, the contrast agent injection time is acquired from “Contrast / Bolus Start Time” defined by the tags (0018, 1042), and the imaging start time is acquired from “Acquisition Time” defined by the tags (0008, 0032). .

  The imaging time phase estimation unit 31 to which the contrast medium injection start time and the imaging start time acquired in the time information acquisition unit 30 are transferred calculates the difference between the imaging time and the contrast agent injection time, and the imaging time phase shown in FIG. The photographing time phase is determined with reference to the calculation table A. The example shown in FIG. 4 is a table classified into one of the four phases of early arterial phase / late arterial phase / portal vein phase / venous phase, but the time phase is set to an appropriate number and time zone according to the application. To do.

The determination of the region extraction process type by the region extraction process type determination unit 32 will be described.
FIG. 5 is an image diagram of medical image volume data obtained by imaging the abdomen of a patient with liver cancer, and shows a difference in images in two phases of the late arterial phase and the venous phase. The difference in hatching is the CT value (the CT value is the value of the X-ray attenuation coefficient (X-ray absorption)) based on water, water is set to 0, the bone is set to 1000, the specific gravity between them is The CT values are set to have a linear relationship.) After contrast agent injection, the CT value of the tumor part becomes high, and the tumor part shows a high CT value in the late arterial phase. As time elapses, the contrast agent flows out of the tumor area, and in the venous phase, the entire liver exhibits a high CT value. In the venous phase, the tumor part often exhibits a lower CT value than in the late arterial phase. In the automatic region extraction algorithm, a difference between pixel values of a region to be extracted and its peripheral portion, that is, a tumor portion and a liver parenchymal portion in the example of FIG. 5 is used. In general, the tumor portion has a CT value higher than that of the liver parenchyma in the late arterial phase, and a CT value lower than that of the liver parenchyma in the venous phase. Accordingly, an extraction process type based on a change in pixel value corresponding to a change in CT value must be selected according to each time phase.

  In the extraction process type determination table A shown in FIG. 6, an appropriate extraction algorithm and parameters necessary and necessary for executing the extraction algorithm are set for each shooting time phase. Based on the shooting time phase transferred from the shooting time phase estimation unit 31, the extraction processing type determination table A is referred to, and the extraction algorithm and parameter values are determined.

  Here, the case where Region Growing is used as an extraction method will be described as an example. With Region Growing, one or several points called seeds are set automatically or manually, and the pixel value of the neighboring pixel is referenced from the seed. If the pixel value meets the set conditions, the region is expanded to that pixel. It is a way to go. Algorithm A here is the target pixel <adjacent pixel, or (target pixel-adjacent pixel) <LocalThreshold, and (seed-adjacent pixel) <GlobalThreshold, and the algorithm B is the target pixel> adjacent pixel. Alternatively, the algorithm is such that the region is expanded when (adjacent pixel-target pixel) <LocalThreshold, and the region is expanded when (adjacent pixel-seed) <GlobalThreshold. The parameters here are a and b, where a is LocalThreshold and parameter b is GlobalThresold. The early arterial phase is more likely to have less penetration of the contrast agent into the tumor part than the later arterial phase, so the difference between the pixel in the tumor part and the surrounding pixels is a relatively small value, so parameter a And b are set at lower values, and the contrast between the pixel in the tumor part and the surrounding pixels is relatively large because the late arterial phase is more likely to have a higher concentration of contrast agent penetration into the tumor part than the early arterial phase. Therefore, the value is set higher than the parameters a and b for the early arterial phase.

  Further, as another example of changing the parameter according to the photographing time phase, a case where an active contour model (Active Contour Model, hereinafter ACM) is used as an extraction method will be described. ACM sets the contour line C (closed curve connecting n discrete point sequences) as the initial value near the boundary of the target area, and after defining the energy function E that is an evaluation function for C, This is an extraction algorithm in which the position and shape of C are corrected so that the value of E becomes small, and C that minimizes E is used as the boundary of the target region. E is expressed by the sum of energy Ein = αEcon + βEcur representing the continuity and smoothness of the closed curve, and the integral value of energy Eim based on the edge strength of the image. Econ is the square of the first derivative of C and represents the continuity of the closed curve, and Ecur is the square of the second derivative of C and represents the smoothness of the closed curve. Eim represents the subtraction of the gradient of the pixel value at a certain point, and becomes smaller at a place where there is an edge. Therefore, α and β are set to be small in order to enhance edge strength when the contour is to be faithfully set to the pixel value of the image, and α and β are to be emphasized when the smoothness of the contour is more important than the fidelity to the pixel value. β is set large.

  When the entire liver is extracted using this ACM, α and β are parameters a and b. In the arterial phase shortly after the contrast agent injection time, there is a high possibility that the contrast agent has not spread throughout the liver, so the difference between the liver parenchyma pixel values and the surrounding pixel values is not very large, and the edge strength It is necessary to have a contour that emphasizes smoothness and continuity. For this reason, a and b are set to be higher values so that Ein is emphasized than Eim and Eex. On the other hand, in the portal vein phase and the delayed phase after the contrast agent injection time, there is a high possibility that the contrast agent penetrates the entire liver, so the difference between the pixel value of the liver parenchyma and the pixel value of the surrounding tissue is It will be big. Therefore, the extraction accuracy can be improved by setting a and b as lower values than in the case of the arterial phase and by using an energy function that emphasizes Eim and Eex.

  In addition to this, for example, when extracting by using a method such as using multi-time phase volume data and taking a difference between volume data of different time phases, there are a plurality of input volume data corresponding to the number of time phases. Similarly, in this case, the time information acquisition unit 30 acquires time information from each input volume data, and the shooting time phase estimation unit 31 estimates the shooting time phase of each volume data based on the acquired time information.

  Here, the above is an example in which four imaging time phases generally used at the time of image diagnosis are estimated from time information, but when each time phase is defined as in the imaging time phase calculation table A shown in FIG. For example, even in the same portal vein phase, there is a width of 100 [sec] between them, and the CT of the same part is taken depending on whether it was taken at an early stage or later at 100 [sec]. The value may change and the features that appear in the image may be different. In order to cope with such a case, the photographing time phase is further divided and defined.

  FIG. 7 shows an example of a photographing time phase calculation table B in which the above four photographing time phases are defined separately for the first and second periods. By setting the time phase in this way, it is possible to set the extraction process type at a more detailed time interval, as illustrated in the extraction process type determination table B of FIG. Image processing can be executed.

  In addition, when photographing is performed at a time deviating from the generally applied photographing timing, there is a possibility that the patient or the affected part does not have the characteristics necessary for the automatic extraction processing according to the set extraction processing type. For example, as shown in FIG. 5, the time taken for the inflow and outflow of the contrast agent to the affected part or organ is shorter or longer than the generally considered time. In this case, in the processing based on the extraction processing type assuming that the influence of the contrast agent becomes a general behavior and appears in the CT value, there is a possibility that automatic extraction fails or the extraction accuracy is low. .

The imaging time phase calculation table C shown in FIG. 9 is an example of an imaging calculation table for estimating imaging timing and automatic extraction likelihood from (imaging time-contrast medium injection time) [sec] in addition to the imaging time phase. The automatic extraction likelihood is set as a measure of the likelihood of the automatic extraction result. If the automatic extraction likelihood is high, the likelihood is high (the possibility of successful extraction is high), and if it is low, the likelihood is not high. (It is highly likely that the extraction has failed).
The shooting time phase estimation unit 31 can estimate the automatic extraction likelihood by referring to the shooting time phase calculation table C. For example, this information is transferred to the image processing algorithm execution unit 22 to transfer the automatic extraction likelihood. Simultaneously with the display, the likelihood of automatic extraction is displayed as a reference value to the operator.

  Alternatively, information on the photographing timing estimated by the photographing time phase estimation unit 31 can be transferred to the region extraction processing type determination unit 32. The area extraction process type determination unit 32 refers to the extraction process type determination table C illustrated in FIG. 10 and determines an optimum area extraction process type including information on the photographing timing.

  In addition to the time information, the DICOM header includes physical information such as the subject's weight, height, age, and the like. The area extraction process type determination unit 32 can determine an extraction process type with higher accuracy by referring to these pieces of information. For example, a subject with a high height or a subject with a heavy weight is generally considered to have a larger body volume compared to a subject with a short height or a subject with the same height and low weight. It can be said that the physical distance to the organ or diseased site is generally farther. Therefore, even if the time from the start of contrast medium injection to the start of imaging is the same, it can be considered that the flow of contrast medium into the extraction target region is relatively small for subjects with a high height or subjects with a heavy weight. For this reason, improvement in extraction accuracy can be expected by adjusting the extraction processing type according to the body volume of the subject.

FIG. 11 shows an example of a physique index calculation table when the physique index is calculated from the height and weight, and an example of an extraction parameter correction formula when the extraction parameter is corrected using the physique index. However, A _n in the example of the extraction Paramemeta correction equation is an example of a body mass index calculated by the body mass index calculation table, p _r shows an example of the extracted parameters determined by the extraction process type determination table. The area extraction process type determination unit 32 uses the physique index obtained from this table to correct each extraction parameter determined in the extraction process type determination table according to the extraction parameter correction formula. As described above, when the subject's body is large, it takes a long time for the contrast medium to reach the affected area, and it also takes a long time for the contrast medium to escape from the affected area. In this case, the extraction accuracy may be improved by setting the parameters a and b to small values.

  In addition, the DICOM tag includes Contrast / Bolus Volume defined by tags (0018, 1041). This is an item indicating the amount of contrast medium injected, but the amount of contrast medium is larger than the amount generally used. When there is a large amount, it is considered that the time until the contrast agent completely leaves the tissue is longer than in the general case. The region extraction process type determination unit 32 can also use an extraction parameter adjusted according to the contrast medium amount.

  In addition to this, as information attached to a medical image, for example, information described in an electronic medical record can be acquired in addition to a DICOM tag. For example, in the treatment by chemotherapy, depending on the medicine or the like injected in the course of treatment, a unique feature may be displayed on the medical image. As an example, Lipiodol Hepatic Arterial Embolization (TAE) is recommended to mix one kind of lymphatic contrast agent called Lipiodol when performing it, Lipiodol usually flows out of the affected area in a few days, It has the property of staying in classic hepatocellular carcinoma. When automatic extraction processing is performed for the purpose of tumor extraction, an untreated tumor region and a tumor region after TAE treatment need to be recognized as different. When it is described in the electronic medical record whether or not TAE treatment is performed as order information, it is possible to determine whether or not lipiodol has been injected. Therefore, the region extraction process type determination unit 32 uses the treatment order information in the extraction process. It can be reflected.

  As described above, the image processing method, the image processing apparatus, and the program that reduce the manual processing by acquiring time information from the medical image volume data in the extraction process when extracting a specific region from the three-dimensional medical image. Can be provided.

  10: external storage device, 11: image processing device, 12: input device, 13: display device, 20: internal memory, 21: automatic area extraction unit, 22: image processing algorithm execution unit, 30: time information acquisition unit, 31 : Shooting time phase estimation unit, 32: region extraction process type determination unit, 33: region extraction process execution unit.

Claims (12)

An image processing apparatus that applies a specific image processing algorithm to input volume data of a medical image,
Time information acquisition means for acquiring time information related to the imaging time of the medical image from the input volume data;
Based on the time information acquired by the time information acquisition means, imaging time phase estimation means for estimating the imaging time phase of the medical image;
A region extraction algorithm for extracting an image processing target region from the medical image according to the photographing time phase estimated by the photographing time phase estimation means, and a region extraction processing type including parameters necessary for executing the region extraction algorithm Region extraction processing type determination means for determining
A region extraction unit having a region extraction processing execution unit for performing region extraction processing on the input volume data by the region extraction processing of the type determined by the region extraction processing type determination unit;
An image processing apparatus, comprising: an image processing algorithm execution unit that executes the specific image processing algorithm on the volume data of the region extracted by the region extraction processing execution means. The image processing apparatus according to claim 1,
When the input volume data is a plurality of volume data corresponding to a multi-slice medical image, the time information acquisition means acquires the time information from each of the plurality of volume data,
The imaging time phase estimation means estimates each imaging time phase corresponding to the multi-slice medical image based on each time information acquired by the time information acquisition means,
The image processing apparatus according to claim 1, wherein the region extraction processing type determination unit determines each of the region extraction processing types according to each of the shooting time phases estimated by the shooting time phase unit. The image processing apparatus according to claim 1,
The shooting time phase estimation means estimates a quasi-shooting time phase defined by a time interval finer than the shooting time phase,
The image processing apparatus according to claim 1, wherein the extraction processing type determination unit determines the region extraction processing type according to the shooting time phase and the semi-shooting time phase estimated by the shooting time phase estimation unit. The image processing apparatus according to claim 1,
The shooting time phase estimation means includes automatic extraction likelihood estimation means for estimating an automatic extraction likelihood of the input volume data using the time information acquired by the time information acquisition means,
The image processing algorithm execution unit displays the automatic extraction likelihood simultaneously with the execution result of the specific image processing algorithm. The image processing apparatus according to claim 1,
The area extraction process type determination means acquires information on a subject associated with the input volume data, and corrects the area extraction process of the determined type using the acquired subject information. apparatus. The image processing apparatus according to claim 1,
The region extraction process type determining means acquires information on a treatment order accompanying the input volume data, and corrects the region extraction process of the determined type using the acquired information on the treatment order. An image processing apparatus. An image processing method for applying a specific image processing algorithm to input volume data of a medical image in an image processing apparatus having an area extraction unit and an image processing algorithm execution unit, wherein the region extraction unit includes:
A time information acquisition step of acquiring time information related to the imaging time of the medical image from the input volume data;
Based on the time information acquired in the time information acquisition step, an imaging time phase estimation step for estimating an imaging time phase of the medical image;
A region extraction algorithm for extracting a region to be image-processed from the medical image according to the photographing time phase estimated in the photographing time phase estimation step, and a region extraction processing type including parameters necessary for executing the region extraction algorithm Region extraction processing type determination step for determining
A region extraction processing execution step for performing region extraction processing on the input volume data by the region extraction processing of the type determined in the region extraction processing type determination step;
The image processing algorithm execution unit executes the specific image processing algorithm on the volume data of the region extracted in the region extraction processing execution step. The image processing method according to claim 7, comprising:
In the time information acquisition step, when the input volume data is a plurality of volume data corresponding to a multi-slice medical image, the time information is acquired from each of the plurality of volume data,
The imaging time phase estimation step estimates each imaging time phase corresponding to the multi-slice medical image based on the time information acquired in the time information acquisition step,
The region extraction processing type determination step determines each of the region extraction processing types according to each of the shooting time phases estimated in the shooting time phase step. The image processing method according to claim 7, comprising:
The pre-shooting time phase estimation step estimates a quasi-shooting time phase defined by a time interval finer than the shooting time phase,
The extraction processing type determination step determines the region extraction processing type according to the shooting time phase and the semi-shooting time phase estimated in the shooting time phase estimation step. The image processing method according to claim 7, comprising:
The imaging time phase estimation step estimates the automatic extraction likelihood of the input volume data using the time information acquired in the time information acquisition step,
The image processing algorithm execution unit displays the previous automatic extraction likelihood simultaneously with the execution result of the specific image processing algorithm. The image processing method according to claim 7, comprising:
The region extraction processing type determination step acquires subject information associated with the input volume data, and corrects the region extraction processing of the determined type using the acquired subject information. Method. The image processing method according to claim 7, comprising:
The region extraction process type determination step acquires information relating to a treatment order accompanying the input volume data, and corrects the region extraction process of the type determined using the acquired treatment order information. Image processing method.

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