Medical image processing method and device, processing equipment and storage medium

Abstract

The application relates to a medical image processing method and device, a processing device and a storage medium. The method comprises the following steps: obtaining a region of interest in a medical image; determining the size of the region of interest based on parameter information of at least one segmentation unit contained in the region of interest, wherein the parameter information comprises the size of the segmentation unit, the number of the at least one segmentation unit and whether each segmentation unit is located in an effective region of the medical image. The medical image processing method provided in the application embodiment improves the accuracy of the size of the region of interest by adding a process of judging whether the segmentation unit is located in the invalid region during the determination of the size of the region of interest, and can help the user to obtain the size of the region of interest more accurately.

Description

TECHNICAL FIELD

[0001] The present application relates to the medical technology field, in particular to a medical image processing method and device, processing equipment and storage medium. BACKGROUND

[0002] Multiplanar Reconstruction (MPR) technology is a technology for displaying any cross section of 3D data, which can truly reflect the scanning results of medical imaging devices such as Computed Tomography (CT) devices, and provides users with main diagnostic basis in clinical and treatment aspects on the basis of traditional cross-sectional images, and more viewing angles.

[0003] Generally, the size of a tumor, the cross-sectional area of a blood vessel and other tissue areas of practical significance can be measured by calculating the area of a Region Of Interest (ROI) on an MPR image. Therefore, it is very important to ensure the accuracy of the calculation of the area of the ROI. However, with the development of medical image post-processing technology, part of the voxels in the MPR can be marked as invisible by tissue extraction on the MPR image, and doctors can also manually set some areas that are not expected to be calculated into the ROI as invisible. Therefore, when calculating the area of the ROI, the areas marked as invisible need to be bypassed, and only the area of the effective region is calculated, which greatly increases the difficulty of accurately calculating the area of the ROI.

[0004] Therefore, the related art urgently needs a medical image processing method with high accuracy. SUMMARY

[0005] Therefore, in view of the above technical problems, the present application provides a medical image processing method, device, processing equipment and storage medium, which can solve the problem of difficulty in accurately calculating the area of the ROI in the related art.

[0006] In a first aspect, an embodiment of the present application provides a medical image processing method, which comprises:

[0007] obtaining a region of interest in a medical image;

[0008] determining the size of the region of interest based on parameter information of at least one segmentation unit contained in the region of interest;

[0009] The parameter information includes the size of the segmentation unit, the number of the at least one segmentation unit, and whether each segmentation unit is located in an effective region of the medical image.

[0010] The medical image processing method provided in the embodiments of the present application can determine the size of the region of interest according to the parameter information of at least one segmentation unit included in the region of interest. Therefore, a smaller segmentation unit can be used to replace a larger original pixel or original voxel when the size of the region of interest is calculated, so that the area (volume) calculation accuracy of the region of interest is improved. In addition, since the process of determining whether the segmentation unit is located in the invalid region is added in the process of determining the size of the region of interest, the accuracy of the size of the region of interest is improved, and a more accurate size of the region of interest can be obtained.

[0011] Optionally, in an embodiment of the present application, the step of determining the size of the region of interest based on the parameter information of at least one segmentation unit included in the region of interest comprises:

[0012] determining whether the region of interest is partially located in the valid region of the medical image;

[0013] in a case where it is determined that the region of interest is partially located in the valid region, determining the size of the region of interest based on the parameter information of at least one segmentation unit included in the region of interest.

[0014] Optionally, in an embodiment of the present application, the step of determining the size of the region of interest based on the parameter information of at least one segmentation unit included in the region of interest comprises:

[0015] determining whether the region of interest is an irregular region;

[0016] in a case where it is determined that the region of interest is an irregular region, determining the size of the region of interest based on the parameter information of at least one segmentation unit included in the region of interest.

[0017] Optionally, in an embodiment of the present application, the step of determining the size of the region of interest based on the parameter information of at least one segmentation unit included in the region of interest comprises:

[0018] determining the size of the region of interest according to the number and size of at least one segmentation unit included in the region of interest and located in the valid region.

[0019] Optionally, in an embodiment of the present application, the segmentation unit is divided in the following manner:

[0020] determining a minimum enclosing region of the region of interest in the medical image;

[0021] dividing the minimum enclosing region into a plurality of segmentation units according to a preset segmentation size or a preset segmentation number.

[0022] Optionally, in an embodiment of the present application, the size of the segmentation unit is set to be smaller than the size of the original pixel or original voxel in the medical image.

[0023] In a second aspect, the embodiments of the present application further provide a medical image processing device, the device comprising:

[0024] an acquisition module, configured to acquire a region of interest in a medical image;

[0025] a determination module, configured to determine the size of the region of interest based on parameter information of at least one segmentation unit contained in the region of interest, wherein the parameter information comprises the size of the segmentation unit, the number of the at least one segmentation unit, and whether each of the segmentation units is located in an effective region of the medical image.

[0026] In a third aspect, the embodiments of the present application further provide a processing device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the medical image processing method according to the above-mentioned embodiments of the present application when executing the computer program.

[0027] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the steps of the medical image processing method according to the above-mentioned embodiments of the present application.

[0028] In a fifth aspect, the embodiments of the present application further provide a computer program product, which comprises computer readable code or a non-volatile computer readable storage medium carrying computer readable code, and when the computer readable code is run in a processor of an electronic device, the processor in the electronic device executes the steps of the medical image processing method according to the above-mentioned embodiments of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0030] Figure 1 is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0031] Figure 2 is a method flowchart of a medical image processing method provided by an embodiment of the present application;

[0032] Figure 3 is a raw pixel segmentation schematic diagram provided by an embodiment of the present application;

[0033] Figure 4 is a schematic diagram of a region of interest containing volume data provided by an embodiment of the present application;

[0034] Figure 5 is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0035] Figure 6 is a schematic diagram of a module structure of the medical image processing apparatus 103 provided by an embodiment of the present application;

[0036] Figure 7 is a schematic diagram of a module structure of the processing device 700 provided by an embodiment of the present application;

[0037] Figure 8 is a conceptual partial view of the computer program product 800 provided by an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is described and explained below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application. In addition, it can be understood that, although the efforts made in this development process can be complex and lengthy, some designs, manufacturing or production changes made on the basis of the technical content disclosed in the present application by those of ordinary skill in the art related to the content disclosed in the present application are only routine technical means and should not be understood as insufficient disclosure of the present application.

[0039] In the present application, "embodiment" means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment to other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in the present application can be combined with other embodiments without conflict.

[0040] Unless otherwise defined, technical terms and scientific terms used in the present application shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. Unless otherwise defined, the terms "a", "an", "one", "this", and "that" used in this application are not intended to be singular or to limit the number of entities. The terms "including", "containing", "having" and their variants are intended to be open-ended terms that allow for items to be added to or removed from the list of components, steps, or modules (units) described. For example, a process, method, product, or apparatus that comprises a list of steps or modules (units) is not limited to those steps or modules (units) that have been expressly listed, but can include additional steps or modules (units) that are inherent in such process, method, product, or apparatus. The terms "connected", "coupled", and "linked" are not restricted to direct or physical connections or associations. The term "plurality" means two or more. The terms "first", "second", "third", and the like are not intended to denote a particular order or ranking, but are used to distinguish one object from another.

[0041] See Figure 1 , Figure 1is a kind of application scenario schematic diagram provided by the embodiment of the application, the application scenario can include acquisition device 101 and medical image processing device 103, wherein the acquisition device 101 and the medical image processing device 103 can communicate, to send the medical image collected to the medical image processing device 103, by the medical image processing device 103, the determination of the size of region of interest is completed.The medical image processing device 103 can process the medical image collected by the acquisition device 101 using the medical image processing method provided by any of the following embodiments of the application.The specific medical image processing device 103 can be partially segmented after obtaining the medical image, at least one segmentation unit is determined, so that the size of the region of interest can be determined according to the parameter information of at least one segmentation unit contained in the region of interest.The acquisition device 101 can be an electronic device with data acquisition capability and data transceiver capability.For example, the acquisition device 101 can include an electronic device capable of collecting medical images, such as computed tomography (Computed Tomography, CT) equipment, magnetic resonance imaging equipment (Magnetic Resonance Imaging, MRI), positron emission computed tomography (Positron Emission Computed Tomography, PET) and the like, or a multi-mode imaging device composed of the above-mentioned various electronic devices, such as PET-CT device, PET-MRI device and the like.The medical image collected by the acquisition device 101 can be a computer tomography image, a magnetic resonance image or a positron emission tomography image, etc.The medical image processing device 103 can be an electronic device with data processing capability and data transceiver capability, and the electronic device can be a physical device or a physical device cluster, such as a server or a server cluster, etc.Of course, the electronic device can also be a virtual cloud device, such as at least one cloud computing device in a cloud computing cluster, and the form of the electronic device is not limited in the application.

[0042] The medical image processing method described in the application will be described in detail below with reference to the accompanying drawings. Figure 2 is a flowchart of an embodiment of the medical image processing method provided by the application.Although the method operation steps are provided as described in the following embodiments or drawings, more or fewer operation steps can be included in the method based on conventional or non-creative labor.There is no necessary causal relationship between the steps in logic, and the execution order of these steps is not limited to the execution order provided by the embodiments of the application.The method can be executed in parallel (such as parallel processor or multi-thread processing environment) in the actual medical image processing process or method execution according to the method order shown in the embodiments or drawings.

[0043] In particular, one embodiment of the medical image processing method provided by the present application can include the following steps. Figure 2 As shown in the method can include:

[0044] S201: Obtain a region of interest in a medical image.

[0045] In the embodiments of the present application, the medical image can be obtained by scanning a target object by the acquisition device 101 and reconstructing the obtained raw data. The target object can be a human body or an animal body, etc. The data reconstruction method can include Multiplanar Reconstruction (MPR), Marching Cubes (MC) algorithm, Maximal Intensity Projection (MIP), etc. Specifically, the multiplanar reconstruction can include superimposing all axial images in a scanning range and then performing coronal, sagittal, or arbitrary angle oblique image reconstruction on a tissue specified by a recombination line of some reticle calibration. The storage format of the medical image can include Digital Image Correlation Method (DICOM) format, Analyze format, NIfTI format, etc., which are not limited in the present application. The medical image can be a two-dimensional medical image or a three-dimensional medical image. In an embodiment of the present application, the two-dimensional medical image can be obtained by real-time scanning the target object by the acquisition device 101. For example, the two-dimensional medical image can be generated by performing mutual parallel, continuous, non-interval, and non-overlapping thin layer scanning on a target human tissue and / or organ by the CT device. That is, the two-dimensional medical image can be a sagittal, coronal, or transverse two-dimensional medical image generated by the acquisition device 101, for example, 200 CT images with a layer thickness of 1.25 mm. The three-dimensional medical image can be a three-dimensional medical image generated by a group of two-dimensional medical images based on a rendering algorithm. Correspondingly, in the two-dimensional medical image, the finest distinguishable element can be a pixel. In the three-dimensional medical image, the finest distinguishable element can be a voxel. The region of interest (ROI) is a region of interest of a user in the medical image, for example, the region of interest can be a tumor region, a nodule region, a polyp region, a lesion region, etc. It should be noted that, since the medical image can be a three-dimensional medical image or a two-dimensional medical image, correspondingly, the region of interest can be a three-dimensional region or a two-dimensional region. In an embodiment of the present application, the region of interest can be a region selected by a user through an input device connected to the terminal device, or a closed region generated by a magnetic lasso, spline interpolation, etc. on the terminal device. The input device can include a mouse, a keyboard, a touchpad, a handwriting input device, etc. In another embodiment of the present application, the region of interest can also be a region selected by a user on a touch screen device by a finger or a stylus.Of course, in a virtual reality scene such as Augmented Reality (AR), the region of interest can also be a region selected by a user through a ray pen, a handle, a sensing glove or the like. It should be noted that in the case where the region of interest is a three-dimensional region, the region selected by the user in the AR virtual scene can be obtained.

[0046] S203: Determine the size of the region of interest based on the parameter information of at least one segmentation unit contained in the region of interest; wherein the parameter information includes the size of the segmentation unit, the number of the at least one segmentation unit, and whether each segmentation unit is located in the effective region of the medical image.

[0047] In actual application, the size (area or volume) of the region of interest can be determined by the number and size of the original pixels or voxels contained therein. However, this results in that the size of the region of interest determined by using the conventional calculation method is not accurate when the region of interest is partially located in the medical image. In addition, the size of the region of interest determined by using the conventional calculation method is also not accurate when the region of interest contains invisible regions, i.e., part of the region of interest is located in the invalid region. The invalid region can be a region in the medical image that does not contain feature information or contains feature information less than a preset threshold. For example, the invalid region can be an edge region of the medical image, a black background, etc. Of course, the invalid region can be a region of no interest marked by a user. For example, a doctor can mark a region other than a tumor region as an invisible region for the convenience of diagnosis and to ensure the accuracy of diagnosis. That is, the size and number of the original pixels or voxels contained in the region of interest cannot accurately represent the size of the region of interest, but also depends on whether the original pixels or voxels contained in the region of interest are located in the valid region. Based on this, in the embodiments of the present application, in order to improve the accuracy of calculating the size of the region of interest, at least part of the medical image can be segmented to form a plurality of segmentation units, and the size of the region of interest can be determined according to the parameter information of at least one segmentation unit contained in the region of interest. Specifically, the medical image can be divided into a plurality of segmentation units. Of course, in order to save storage resources and improve the division efficiency, part of the medical image can be divided to form a plurality of segmentation units, that is, the segmentation units can be obtained by dividing at least part of the medical image. In an embodiment of the present application, the valid region in the medical image can be divided to form a plurality of segmentation units. In other embodiments of the present application, in order to ensure the accuracy of subsequently calculating the size of the region of interest, only the minimum enclosing region of the region of interest in the medical image can be divided to form a plurality of segmentation units, and the present application does not limit the division manner of the medical image. It should be noted that when the same size region is divided, the more the number of the segmentation units obtained, the higher the calculation accuracy of the region of interest.

[0048] In the embodiments of the present application, the parameter information of the segmentation unit can include the size such as area or volume of the segmentation unit, the number of at least one segmentation unit contained in the region of interest, and whether each segmentation unit is located in the valid region of the medical image. The size of the segmentation unit can be set by a user, or can be determined according to the size of the valid region and the segmentation number. In an embodiment of the present application, the valid region of the medical image can include the remaining region of the medical image after removing the invalid region. In another embodiment of the present application, the valid region can also be a region of interest marked by a user, for example, a tissue or an organ contained in the medical image. The tissue can include epithelial tissue, connective tissue, muscle tissue, neural tissue, etc., and the organ can include sensory organs, internal organs, blood vessels, skin, bones, etc. It should be noted that in some embodiments of the present application, the parameter information can also include position information of a plurality of segmentation units. The position information can be position coordinates at the edge position of the segmentation unit, or can be position coordinates at the center position of the segmentation unit. In order to quickly and accurately obtain the parameter information in the subsequent calculation of the size of the region of interest, improve the calculation efficiency, the parameter information can be stored in a local database or cloud, and can also be cached in the memory of the host.

[0049] The medical image processing method provided in the embodiments of the present application can determine the size of the region of interest according to the parameter information of at least one segmentation unit contained in the region of interest. Therefore, smaller segmentation units can be used to replace larger original pixels or original voxels when calculating the size of the region of interest, thereby improving the area (volume) calculation accuracy of the region of interest. In addition, since the process of determining whether the segmentation unit is located in the invalid region is added in the process of determining the size of the region of interest, the accuracy of the size of the region of interest is improved, which can help the user to obtain more accurate size of the region of interest.

[0050] In an embodiment of the present application, the size of the region of interest can be determined according to the number and size of the segmentation units contained in the region of interest and located in the valid region. Specifically, the determination of the size of the region of interest based on the parameter information of at least one segmentation unit contained in the region of interest can include:

[0051] S301: determining the size of the region of interest according to the number and size of at least one segmentation unit contained in the region of interest and located in the valid region.

[0052] In the embodiments of the present application, whether the segmentation unit contained in the region of interest is located in the effective region can be determined in sequence, and the number of segmentation units located in the region of interest is counted. Specifically, the segmentation units contained in the region of interest can be scanned in sequence by using a scan line filling algorithm or a graph filling algorithm, so as to determine whether the segmentation units are located in the effective region. Of course, in an embodiment of the present application, whether the segmentation unit is located in the effective region can be determined based on the identification information of the volume data. Specifically, in the case that the identification information meets the preset requirement, it can be determined that the segmentation unit is located in the effective region. The identification information can be the unique identification information of the effective region or the invalid region. It should be noted that the identification information of the effective region is not the same as that of the invalid region. Specifically, the effective region and the invalid region can be given different identification information according to the actual application scenario. For example, in the case of needing to determine the size of the blood vessel region in the medical image, the blood vessel tissue region can be marked as 1, and other tissue regions can be marked as 0. In the case that the region label where the segmentation unit is located is 1, it can be determined that the segmentation unit is located in the effective region. In an embodiment of the present application, after the number of segmentation units contained in the region of interest and located in the effective region is determined, the size of the region of interest can be determined according to the number and the size of the segmentation unit. In an embodiment of the present application, the product value of the number and the size of the segmentation unit can be taken as the area or volume of the region of interest. For example, in an example, in the case that the number of segmentation units contained in the region of interest is 100000 and all of them are located in the effective region, and the size of the segmentation unit is 0.02mmx0.02mm, the size of the region of interest is 40mm 2 . Of course, in the case that 50000 segmentation units of the 100000 segmentation units are located in the invalid region, the size of the region of interest can be determined as 20mm 2In an embodiment of the present application, a coordinate system of the region of interest can also be established, and the size of the region of interest can be determined according to the unit area or unit volume of the coordinate system contained in the region of interest. The coordinate system can be a two-dimensional coordinate system or a three-dimensional coordinate system, which can be determined according to the dimension of the region of interest. Taking the region of interest as a three-dimensional region as an example, specifically, in an embodiment of the present application, the unit length of the x-axis, y-axis and z-axis of the three-dimensional coordinate system can be determined according to the side length of the segmentation unit, and the boundary region of the three-dimensional coordinate system can be determined according to the boundary position information of the region of interest. After the three-dimensional coordinate system is determined, the volume of the region of interest can be determined according to the number of unit volumes contained in the region of interest and the volume size of the unit volume. It should be noted that the origin or positive direction of the coordinate system does not affect the accuracy of the size of the region of interest.

[0053] According to the above embodiment, the size of the region of interest can be determined according to the number and size of the segmentation units located in the effective region in the region of interest, and the size of the region of interest determined in this way has higher accuracy and precision and is more widely applicable.

[0054] In an embodiment of the present application, in order to make the division of the segmentation unit more reasonable and thus make the size of the region of interest determined more accurate, the segmentation unit can be divided in the following manner:

[0055] S401: determining a minimum enclosing region of a region of interest in the medical image;

[0056] S403: dividing the minimum enclosing region into a plurality of segmentation units according to a preset segmentation size or a preset segmentation number.

[0057] In an embodiment of the present application, when the region of interest is a two-dimensional region, the minimum enclosing region can include a minimum enclosing frame, and when the region of interest is a three-dimensional region, the minimum enclosing region can include a minimum enclosing box. The minimum enclosing frame or the minimum enclosing box can be a regular shape, and the shape of the minimum enclosing frame or the minimum enclosing box can be determined according to the shape of the region of interest. For example, the minimum enclosing frame can be a circle, a rectangle, a triangle, an ellipse, or the like. The minimum enclosing box can be a sphere, a cone, a cube, or the like. Specifically, in an embodiment of the present application, a bounding box algorithm can be used to determine the minimum enclosing region of the region of interest. The bounding box algorithm can be an algorithm for solving the optimal enclosing space of a discrete point set, and the basic idea is to approximately replace a complex geometric object with a geometric body or geometric figure with slightly larger volume and simple characteristics. The bounding box algorithm can include a cuboid bounding box algorithm, a sphere bounding algorithm, and a fixed direction hull (FDH) algorithm. The cuboid bounding box algorithm can include, but is not limited to, an axis-aligned bounding box (AABB) algorithm and an oriented bounding box (OBB) algorithm. After determining the minimum enclosing region of the region of interest, the minimum enclosing region can be divided into a plurality of segmentation units according to a preset segmentation size or a preset segmentation number. It can be understood that in an embodiment of the present application, the preset segmentation number can be determined according to the calculation accuracy of the region of interest and the performance of the processing device. For example, when a user needs high measurement accuracy, a larger preset segmentation number such as 1,000,000, 1,500,000, or the like can be set. After determining the preset segmentation number, the size of the segmentation unit can be determined according to the size of the minimum enclosing region and the preset segmentation number. In this way, the number of segmentation units obtained by dividing different sizes of bounding boxes can be ensured to be the same, so that the running time can be ensured to be substantially the same when determining the size of the region of interest in different cases, and the user can complete real-time interaction, so that the user has a more smooth interactive experience. For example, in an example, the preset segmentation number can be set to 10,000, and when the minimum enclosing region is 100 mm 2 , the size of the segmentation unit can be determined to be 0.01 mm 2 . Of course, when the minimum enclosing region is 50 mm 2 , the size of the segmentation unit can be determined to be 0.005 mm 2In other embodiments of the present application, the size of the segmentation unit can also be determined according to the preset segmentation size, and the number of segmentation units obtained by dividing the minimum enclosing region can be determined according to the preset segmentation size. The preset segmentation size can be determined by a user according to the calculation speed and the measurement accuracy, for example, the preset segmentation size can be set to 0.02 mm 2 , 0.03 mm 2 . Specifically, after the segmentation size is determined, the corresponding number can be determined, and the minimum enclosing region can be segmented based on the segmentation size and the number. The segmentation manner can include uniform segmentation, non-uniform segmentation, and the like. It should be noted that the present application does not limit the division manner of the minimum enclosing region, nor does it limit the specific form of the segmentation unit. The minimum enclosing region can only be divided into a plurality of segmentation units.

[0058] According to the above embodiments, the minimum enclosing region of the effective region can be segmented into a plurality of segmentation units according to the preset segmentation size or the preset segmentation number. Since the preset segmentation size or the preset segmentation number can be set by a user, the user can determine a more reasonable preset segmentation size or preset segmentation number according to the measurement accuracy and the calculation speed, so that the size of the region of interest determined is more in line with the requirements.

[0059] In an embodiment of the present application, the size of the segmentation unit can be set to be smaller than the size of the original pixel or the original voxel in the medical image.

[0060] In the embodiments of the present application, in order to ensure the measurement accuracy of the size of the region of interest, the size of the segmentation unit can be set to be smaller than the size of the original pixel or the original voxel in the medical image. For example, in order to improve the measurement accuracy by four times, the size of the segmentation unit can be set to one fourth of the size of the original pixel. For example, as shown in FIG. 3, the original pixel 303 can be segmented into a plurality of segmentation units 301 according to the segmentation manner in the figure. In an embodiment of the present application, the smaller the size of the segmentation unit is set, the higher the size accuracy of the region of interest determined is. Therefore, the size of the segmentation unit can be set based on the balance between the measurement accuracy and the calculation speed. Figure 3

[0061] In an embodiment of the present application, the determination of the size of the region of interest based on the parameter information of at least one segmentation unit included in the region of interest can include:

[0062] S501: determining whether the region of interest is partially located in the effective region of the medical image;

[0063] ​S503: In a case where it is determined that the region of interest is partially located in the effective region, determining the size of the region of interest based on parameter information of at least one partition unit contained in the region of interest.

[0064] In the embodiments of the present application, it can be determined whether the region of interest is located in the effective region before determining the size of the region of interest. In a case where the region of interest is partially located in the effective region, the size of the region of interest can be determined according to the method of determining the size of the region of interest as described above. Of course, in a case where the region of interest is completely located in the effective region, in order to save storage resources and improve the calculation speed, the size of the region of interest can be calculated by using a conventional area calculation method. For example, the size of the region of interest can be determined by using the number of original pixels contained in the region of interest and the size of the original pixels.

[0065] It should be noted that, in an embodiment of the present application, as shown in Figure 4 in a case where the region of interest 401 contains the entire volume data 403, the size of the volume data 403 can be taken as the size of the region of interest 401, so that the area calculation speed of the region of interest can be improved.

[0066] Through the above embodiments, it can be determined whether the region of interest is partially located in the effective region, so that different calculation methods can be selected to determine the size of the region of interest according to different determination results, thereby improving the accuracy of the method of determining the size of the region of interest.

[0067] In an embodiment of the present application, the determining the size of the region of interest based on the parameter information of at least one partition unit contained in the region of interest can include:

[0068] S601: determining whether the region of interest is an irregular region;

[0069] S603: In a case where it is determined that the region of interest is an irregular region, determining the size of the region of interest based on parameter information of at least one partition unit contained in the region of interest.

[0070] In the embodiments of the present application, the region of interest can be a regular region or an irregular region. In the case where the region of interest is a two-dimensional region, the regular region can be a circle, an ellipse, a rectangle, etc. Of course, in the case where the region of interest is a three-dimensional region, the regular region can be a sphere, a cube, a cone, etc. In one embodiment of the present application, before calculating the size of the region of interest, it is necessary to determine whether the region of interest is an irregular region. In one embodiment of the present application, in the case where the region of interest is a regular region, in order to improve the operation efficiency and save storage resources, the size of the region of interest can be determined by using a conventional area calculation method. For example, in the case where the region of interest is a circle, the size of the region of interest can be determined by using the formula S = πr 2 determining the area of the region of interest. In the case where the region of interest is a sphere, the volume of the region of interest can be determined by using the formula V = (4 / 3)πR 3 determining the volume of the region of interest.

[0071] It should be noted that the determination processes of steps S501-S503 and steps S601-S603 described in the above embodiments can be performed simultaneously. For example, it can be simultaneously determined whether the region of interest contains an invalid region and whether the region of interest is an irregular region, and in the case where any one of the determination results is yes, the size of the region of interest can be determined by using the medical image processing method described in each embodiment of the present application. In one embodiment of the present application, as shown in FIG. 4, in the case where the region of interest 401 is a regular region and the region of interest 401 is entirely located in the valid region 501, the size of the region of interest 401 can be determined by using a conventional calculation method, for example, the area of the region of interest 401 can be determined by using an area calculation formula. Figure 5

[0072] The medical image processing method provided by the present application is described in detail above, and the medical image processing device 103 provided by the present application will be described below with reference to the accompanying drawings. Figure 6 The medical image processing device 103 provided by the present application comprises:

[0073] The acquisition module 601 is configured to acquire a region of interest in a medical image.

[0074] The determination module 603 is configured to determine the size of the region of interest based on parameter information of at least one segmentation unit contained in the region of interest, wherein the parameter information comprises the size of the segmentation unit, the number of the at least one segmentation unit, and whether each segmentation unit is located in a valid region of the medical image.

[0075] ​Optionally, in an embodiment of the present application, the determining the size of the region of interest based on the parameter information of the at least one partition unit contained in the region of interest comprises:

[0076] determining whether the region of interest is partially located in the valid region of the medical image;

[0077] in a case where it is determined that the region of interest is partially located in the valid region, determining the size of the region of interest based on the parameter information of the at least one partition unit contained in the region of interest.

[0078] Optionally, in an embodiment of the present application, the determining the size of the region of interest based on the parameter information of the at least one partition unit contained in the region of interest comprises:

[0079] determining whether the region of interest is an irregular region;

[0080] in a case where it is determined that the region of interest is an irregular region, determining the size of the region of interest based on the parameter information of the at least one partition unit contained in the region of interest.

[0081] Optionally, in an embodiment of the present application, the determining the size of the region of interest based on the parameter information of the at least one partition unit contained in the region of interest comprises:

[0082] determining the size of the region of interest according to the number and size of the at least one partition unit contained in the region of interest and located in the valid region.

[0083] Optionally, in an embodiment of the present application, the partition units are divided in the following manner:

[0084] determining a minimum enclosing region of a region of interest in the medical image;

[0085] dividing the minimum enclosing region into a plurality of partition units according to a preset partition size or a preset partition number.

[0086] Optionally, in an embodiment of the present application, the size of the partition unit is set to be smaller than the size of an original pixel or an original voxel in the medical image.

[0087] The medical image processing apparatus 103 according to the embodiments of the present application can correspond to performing the method described in the embodiments of the present application, and the above and other operations and / or functions of each module in the medical image processing apparatus 103 are respectively provided for realizing the corresponding procedures of the method provided in each of the above embodiments, and for the sake of brevity, will not be described here.

[0088] It should be noted that the above-described embodiments are merely illustrative, and the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place or distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments. In addition, the connection relationship between the modules in the device embodiment provided in the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines.

[0089] In addition, in order to better illustrate the present application, a large number of specific details are given in the specific embodiments below. Those skilled in the art should understand that the present application can also be implemented without some specific details. In some examples, devices, means, elements and circuits that are well known to those skilled in the art are not described in detail, in order to highlight the main ideas of the present application.

[0090] Another aspect of the present application also provides a processing device, comprising a memory and a processor, the memory stores computer program instructions, and the processor is configured to execute the computer program instructions to perform the steps of the medical image processing method described in each of the above embodiments.

[0091] Among them, the processing device can be a physical device or a cluster of physical devices, or a virtualized cloud device, such as at least one cloud computing device in a cloud computing cluster. In order to facilitate understanding, the present application takes the processing device as an independent physical device to illustrate the structure of the processing device.

[0092] As shown in Figure 7 The processing device 700 includes a processor and a memory for storing processor computer program instructions; wherein the processor is configured to execute the computer program instructions to realize the above device. The processing device 700 includes a memory 701, a processor 703, a bus 705 and a communication interface 707. The memory 701, the processor 703 and the communication interface 707 communicate through the bus 705. The bus 705 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For the convenience of representation, Figure 7Only one bus is shown among the various buses, but it is understood that the computer system 700 can have several buses. Communication functions of the various buses enable the central processing unit 703, the memory 701, and the input / output device to communicate with one another. The bus 705 is for the communication of external devices with the computer system. The central processing unit 703 can be a central processing unit (CPU). The memory 701 can include a volatile memory, such as a random access memory (RAM), and a non-volatile memory, such as a read-only memory (ROM), a floppy, a compact disk, a hard disk, or a solid state drive.

[0093] Those skilled in the art can understand that Figure 7 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0094] The embodiment of the present application provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method.

[0095] The embodiment of the present application provides a computer program product, including computer readable code or a non-volatile computer readable storage medium carrying computer readable code, when the computer readable code is run in a processor of an electronic device, the processor in the electronic device executes the method.

[0096] In some embodiments, the disclosed method can be implemented as computer program instructions encoded in a computer readable storage medium in a machine readable format or in other non-transitory media or articles of manufacture. Figure 8 A conceptual partial view of an example computer program product arranged in accordance with at least some embodiments presented herein is shown schematically, the example computer program product including a computer program for executing a computer process on a computing device. In one embodiment, the example computer program product 800 is provided using a signal bearing medium 801. The signal bearing medium 801 can include one or more program instructions 802 that, when run by one or more processors, can provide the functionality or some of the functionality described above with respect to Figure 2 The described functionality or partial functionality. In addition, Figure 8 The program instructions 802 in the signal bearing medium 801 also describe example instructions.

[0097] In some examples, the signal bearing medium 801 can comprise a computer- readable medium 803, such as, but not limited to, a hard disk drive, a compact disk (CD), a digital video disk (DVD), a memory, a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electronically erasable programmable read-only memory (EEPROM), a flash memory, and / or a solid-state drive (SSD). In some embodiments, the signal bearing medium 801 can comprise a computer recordable medium 804, such as, but not limited to, a floppy disk, a hard disk, a CD, a DVD, a memory, a ROM, a PROM, an EPROM, an EEPROM, a flash memory, and / or a SSD. In some embodiments, the signal bearing medium 801 can comprise a communications medium 805, such as, but not limited to, a wired communications link, a wireless communications link, and / or a fiber optic cable. Thus, for example, the signal bearing medium 801 can be a wireless form of the communications medium 805 (e.g., a wireless communication medium complying with the IEEE 802.11 standard or another transmission protocol). The one or more program instructions 802 can be, for example, computer-executable instructions or logic-implementing instructions. In some examples, such as for a software application, the one or more program instructions 802 can be downloaded to the computing device from the signal bearing medium 801 via the computer-readable medium 803, the computer recordable medium 804, and / or the communications medium 805. Figure 8 The electronic device described can be configured to provide various operations, functions, or actions in response to the program instructions 802 being conveyed to the computing device via one or more of the computer-readable medium 803, the computer recordable medium 804, and / or the communications medium 805. It will be appreciated that the arrangements described herein are for purposes of example only. As such, those skilled in the art will appreciate that other arrangements and other elements (e.g., machines, interfaces, functions, orders, and

[0098] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operations of possible implementations of apparatuses, systems, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may

[0099] It is also important to note that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by hardware that performs the recited functionality / acts, such as by a circuit or ASIC (Application-Specific Integrated Circuit), or by a combination of hardware and software, such as by a combination of firmware and software, etc.

[0100] Although the present application has been described in connection with various embodiments, it will be understood that the application is capable of further modifications. These and other changes can be made to the application in light of the above-detailed description. The application is not limited to the described embodiments, but rather, the scope of the application is to be determined by the claims. Single processor or other units can implement several items recited in the claims. Certain actions recited in the dependent claims are not to be understood as necessarily being performed in the order in which they are recited.

[0101] Embodiments of the application have been described above with the aid of example illustrations and are not limited to the embodiments shown. The scope of the application encompasses numerous modifications and variations from the embodiments as disclosed and it is intended to include all technical equivalents of the elements described and illustrated. The terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope of the application.

Claims

1. A medical image processing method characterized by, The method comprises: acquiring a region of interest in a medical image; determining a size of the region of interest based on parameter information of at least one segmentation unit contained in the region of interest; wherein the parameter information comprises a size of the segmentation unit, a number of the at least one segmentation unit, and whether each of the segmentation units is located in a valid region of the medical image, and the size of the segmentation unit is set to be smaller than a size of an original pixel or an original voxel in the medical image; the determining of the size of the region of interest based on the parameter information of the at least one segmentation unit contained in the region of interest comprises: determining the size of the region of interest according to the number and the size of the at least one segmentation unit contained in the region of interest and located in the valid region. the determining of the size of the region of interest based on the parameter information of the at least one segmentation unit contained in the region of interest comprises:

2. The method of claim 1, wherein, judging whether the region of interest is partially located in the valid region of the medical image; in a case where it is determined that the region of interest is partially located in the valid region, determining the size of the region of interest based on the parameter information of the at least one segmentation unit contained in the region of interest. the determining of the size of the region of interest based on the parameter information of the at least one segmentation unit contained in the region of interest comprises:

3. The method of claim 1, wherein, judging whether the region of interest is an irregular region; in a case where it is determined that the region of interest is an irregular region, determining the size of the region of interest based on the parameter information of the at least one segmentation unit contained in the region of interest. the segmentation units are divided in the following manner:

4. The method of claim 1, wherein, determining a minimum enclosing region of a region of interest in the medical image; dividing the minimum enclosing region into a plurality of segmentation units according to a preset segmentation size or a preset segmentation number. The device comprises:

5. A medical image processing apparatus characterized by comprising: an acquiring module configured to acquire a region of interest in a medical image; a determining module configured to determine a size of the region of interest based on parameter information of at least one segmentation unit contained in the region of interest; wherein the parameter information comprises a size of the segmentation unit, a number of the at least one segmentation unit, and whether each of the segmentation units is located in a valid region of the medical image, and the size of the segmentation unit is set to be smaller than a size of an original pixel or an original voxel in the medical image; the determining of the size of the region of interest based on the parameter information of the at least one segmentation unit contained in the region of interest comprises: judging whether the region of interest is partially located in the valid region of the medical image; in a case where it is determined that the region of interest is partially located in the valid region, determining the size of the region of interest based on the parameter information of the at least one segmentation unit contained in the region of interest. The processor, when executing the computer program, implements the steps of the medical image processing method in any one of claims 1 to 4.

6. A processing device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor, when executing the computer program, implements the steps of the medical image processing method in any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by a processor, implement the steps of the medical image processing method of any one of claims 1 to 4.

8. A computer program product, characterised in that, The non-volatile computer-readable storage medium including or carrying computer-readable code, when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device executes the steps of the medical image processing method of any one of claims 1 to 4.

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