Medical image processing system and device, operation planning system and storage medium

Abstract

The invention provides a medical image processing system and device, an operation planning system and a storage medium. The medical image processing system comprises an input module, a processing module and a display module. The input module is used for inputting three-dimensional image data of a target object. The processing module is used for obtaining vessel data according to the three-dimensional image data. The processing module is further used for conducting thinning processing on the vessel data when the layer thickness of the vessel data is larger than a first preset value, and the layer thickness of the thinned vessel data is smaller than or equal to the first preset value. And the display module is used for performing superposition display according to the thinned vessel data and the three-dimensional image data. The thinned vessel data layer is small in thickness and rich in data, so that a vessel image with a better form can be reconstructed.

Description

Technical Field

[0001] This application belongs to the field of medical image processing, and particularly relates to a medical image processing system, device, surgical planning system, and storage medium. Background Art

[0002] Image data such as Computed Tomography (CT) and Magnetic Resonance Imaging (MR) are three-dimensional image data. Three-dimensional tissue renderings can be reconstructed based on CT or MR image data to facilitate surgical planning by doctors. Some CT or MR image data are thick-layer image data with a large amount of data loss. When reconstructing images based on thick-layer image data, the morphology of blood vessels in the reconstructed images is somewhat distorted, which can cause certain interference to doctors' decisions. Summary of the Invention

[0003] In view of this, embodiments of this application provide a medical image processing system, device, surgical planning system, and storage medium, which can reconstruct blood vessel images with better morphology based on thick-layer image data.

[0004] The first aspect of the embodiments of this application provides a medical image processing system, including:

[0005] An input module, configured to input three-dimensional image data of a target object;

[0006] A processing module, configured to obtain blood vessel data based on the three-dimensional image data;

[0007] The processing module is further configured to, when the slice thickness of the blood vessel data is greater than a first preset value, perform thinning processing on the blood vessel data, and the slice thickness of the thinned blood vessel data is less than or equal to the first preset value;

[0008] A display module, configured to perform superimposed display based on the thinned blood vessel data and the three-dimensional image data.

[0009] In one embodiment, the processing module is specifically further configured to:

[0010] If the interval between two adjacent pixel points in the first direction in the blood vessel data is greater than a second preset value, it is determined that the slice thickness of the blood vessel data is greater than the first preset value, and the first direction is the same as the slice thickness direction of the blood vessel data.

[0011] In one embodiment, the processing module is specifically further configured to:

[0012] If the ratio of the first interval to the second interval is greater than a preset ratio, it is determined that the slice thickness of the vascular data is greater than a first preset value. The first interval is the interval between two adjacent pixel points in the first direction in the vascular data, the second interval is the interval between the two adjacent pixel points in the second direction, the first direction is consistent with the slice thickness direction of the vascular data, and the first direction is perpendicular to the second direction.

[0013] In one embodiment, the processing module obtaining the vascular data from the three-dimensional image data includes:

[0014] In response to the input of the three-dimensional image data, performing image segmentation on the three-dimensional image data to obtain vascular image data;

[0015] Further, in response to a correction instruction for the vascular image data, correcting the vascular image data to obtain the vascular data.

[0016] In one embodiment, the processing module specifically performing thinning processing on the vascular data includes: inputting the vascular data into a super-resolution model for processing to obtain thinned vascular data.

[0017] In one embodiment, the display module performing superimposed display according to the thinned vascular data and the three-dimensional image data includes:

[0018] Extracting the points located on the center line in the thinned vascular data;

[0019] Rendering the corresponding points as spheres respectively according to the vascular radius corresponding to each point;

[0020] Obtaining the vascular image according to each sphere.

[0021] In one embodiment, the display module performing superimposed display according to the thinned vascular data and the three-dimensional image data includes:

[0022] Reconstructing a vascular image according to the thinned vascular data;

[0023] Reconstructing a tissue image according to the three-dimensional image data;

[0024] Superimposing and displaying the vascular image on the tissue image;

[0025] Or, reconstructing a vascular image according to the thinned vascular data;

[0026] Superimposing and displaying the vascular image on the image corresponding to the three-dimensional image data.

[0027] In a second aspect of the embodiments of the present application, a surgical planning system is provided. The surgical planning system includes a surgical planning module and the medical image processing system as described in the first aspect above. The surgical planning module is configured to plan a surgical path based on the vascular image.

[0028] In a third aspect of the embodiments of the present application, a medical image processing device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following operations are implemented:

[0029] Input three-dimensional image data of a target object;

[0030] Obtain vascular data based on the three-dimensional image data;

[0031] When the slice thickness of the vascular data is greater than a first preset value, perform thinning processing on the vascular data so that the slice thickness of the thinned vascular data is less than or equal to the first preset value;

[0032] Perform overlay display based on the thinned vascular data and the three-dimensional image data.

[0033] In a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following operations are implemented:

[0034] Input three-dimensional image data of a target object;

[0035] Obtain vascular data based on the three-dimensional image data;

[0036] When the slice thickness of the vascular data is greater than a first preset value, perform thinning processing on the vascular data so that the slice thickness of the thinned vascular data is less than or equal to the first preset value;

[0037] Perform overlay display based on the thinned vascular data and the three-dimensional image data.

[0038] In a fifth aspect of the embodiments of the present application, a computer program product is provided. When the computer program product runs on a medical image processing device, the medical image processing device is caused to implement the following operations when executed:

[0039] Input three-dimensional image data of a target object;

[0040] Obtain vascular data based on the three-dimensional image data;

[0041] When the slice thickness of the vascular data is greater than a first preset value, perform thinning processing on the vascular data so that the slice thickness of the thinned vascular data is less than or equal to the first preset value;

[0042] Perform superimposed display based on the thinned vasculature data and the three-dimensional image data.

[0043] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: By acquiring the three-dimensional image data of the target object, obtaining the vasculature data according to the three-dimensional image data, when the slice thickness of the vasculature data is greater than the first preset value, performing thinning processing on the vasculature data to obtain the thinned vasculature data, and then performing superimposed display based on the thinned vasculature data and the three-dimensional image data. Since the thinned vasculature data is obtained by reducing the slice thickness of the vasculature data, the thinned vasculature data retains the original image data. Since the slice thickness of the thinned vasculature data is small and the data is rich, a vasculature image with better morphology can be displayed according to the thinned vasculature data without changing the original three-dimensional image data. Description of the Drawings

[0044] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art.

[0045] Figure 1 is a schematic diagram of a medical image processing system provided by an embodiment of the present application;

[0046] Figure 2 is a schematic diagram of a displayed vasculature image provided by an embodiment of the present application;

[0047] Figure 3 is a schematic diagram of a medical image processing device provided by an embodiment of the present application;

[0048] Figure 4 is a flowchart of a method executed by a processor provided by an embodiment of the present application;

[0049] Figure 5 is an implementation flowchart of a medical image processing method executed by a processor provided by an embodiment of the present application;

[0050] Figure 6 is a schematic diagram of a hepatic vein image reconstructed from thick-layer vasculature data;

[0051] Figure 7 is a schematic diagram of a hepatic vein image reconstructed by a medical image processing system provided by an embodiment of the present application;

[0052] Figure 8 is a schematic diagram of a hepatic portal vein image reconstructed from thick-layer vasculature data;

[0053] Figure 9 is a schematic diagram of a hepatic portal vein image reconstructed by a medical image processing system provided by an embodiment of the present application. Detailed Embodiments

[0054] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0055] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0056] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0057] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0058] In addition, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0059] CT, MR and other imaging data are three-dimensional image data, which include multiple layers of two-dimensional image data. Each layer of two-dimensional image data is obtained by scanning a two-dimensional plane of the scanned object. Two adjacent two-dimensional planes correspond to two adjacent layers of two-dimensional image data, and the distance between two adjacent layers of two-dimensional image data represents the layer thickness. The greater the layer thickness, the more data is lost between two adjacent two-dimensional planes, and the less data is in the three-dimensional image data. When reconstructing an image based on the three-dimensional image data, there is a certain distortion in the morphology of the blood vessels in the reconstructed image. For example, the outer wall of the blood vessel in the reconstructed image appears stepped, which will interfere with the doctor's decision-making.

[0060] To this end, the present application provides a medical image processing system. By acquiring three-dimensional image data of a target object, vascular data is obtained based on the three-dimensional image data. When the slice thickness of the vascular data is greater than a first preset value, thinning processing is performed on the vascular data to obtain thinned vascular data. Then, superimposed display is performed based on the thinned vascular data and the three-dimensional image data. Since the thinned vascular data is obtained by reducing the slice thickness of the vascular data, the thinned vascular data retains the original image data. Since the slice thickness of the thinned vascular data is small and the data is rich, a better-shaped vascular image can be displayed based on the thinned vascular data without changing the original three-dimensional image data.

[0061] The medical image processing system provided by the present application will be described in detail below.

[0062] Please refer to the attached Figure 1 As shown in the figure, the medical image processing system provided by an embodiment of the present application includes an input module 11, a processing module 12, and a display module 13. The input module 11 is used to input three-dimensional image data of a target object. The processing module 12 is used to obtain vascular data based on the three-dimensional image data. The processing module 12 is further used to perform thinning processing on the vascular data when the slice thickness of the vascular data is greater than a first preset value, and the slice thickness of the thinned vascular data is less than or equal to the first preset value. The display module 13 is used to perform superimposed display based on the thinned vascular data and the three-dimensional image data.

[0063] Among them, the three-dimensional image data is obtained by scanning the target object with an imaging device, and the target object can be human tissues, animals, etc. The three-dimensional image data can be CT image data or MR image data.

[0064] Exemplarily, the imaging device scans multiple scan planes of the target object to obtain three-dimensional image data. According to the data corresponding to each scan plane, the three-dimensional image data can be divided into multiple layers of two-dimensional image data, and each layer of two-dimensional image data corresponds to a scan plane. The three-dimensional image data is composed of multiple pixel point data, and each pixel point data includes the coordinates and pixel values of the corresponding pixel point. The vascular data is part of the three-dimensional image data. For example, the vascular data can be three-dimensional image data of the hepatic vein or the hepatic portal vein.

[0065] In one embodiment, the three-dimensional image data is a file in a preset format. The processing module 12 obtaining vascular data based on the three-dimensional image data includes: in response to the input of the three-dimensional image data, performing image segmentation on the three-dimensional image data to obtain vascular data.

[0066] Among them, the vascular data can be a vascular mask. The mask is used to specify a specific area in the image, so that further operations can be performed on the specific area. For example, the pixel value of the pixel points belonging to the blood vessels is set to 1, and the pixel value of the pixel points not belonging to the blood vessels is set to 0. According to the distribution area of the pixel points with a pixel value of 1, the vascular mask can be obtained.

[0067] The processing module 12 can perform image segmentation on the three-dimensional image data by using a vascular segmentation model. The vascular segmentation model is obtained by training a classification model with multiple groups of training samples. The classification model can be a deep neural network model or a convolutional neural network model (such as the nnUNet model), etc. Exemplarily, each group of training samples includes three-dimensional image data and vascular data in the three-dimensional image data. The parameters of the classification model are optimized by using multiple groups of training samples, and the classification model after optimizing the parameters is the vascular segmentation model. The vascular segmentation model is used to identify blood vessels from the input three-dimensional image data and output vascular image data. By using the vascular segmentation model to obtain the vascular image data, the accuracy of the obtained vascular image data can be improved.

[0068] In one embodiment, after obtaining the three-dimensional image data, the processing module 12 can first perform noise reduction processing on the three-dimensional image data, and then use the vascular segmentation model to identify blood vessels from the three-dimensional image data after noise reduction processing to obtain vascular image data, so as to further improve the accuracy of the obtained vascular image data.

[0069] In one embodiment, after the processing module 12 performs image segmentation on the three-dimensional image data to obtain vascular image data, in response to a correction instruction for the vascular image data, the vascular image data is corrected to obtain vascular data.

[0070] Among them, the correction instruction is used to modify the incorrect recognition result. For example, according to the correction instruction, connections are added to connect the same blood vessel, or according to the modification instruction, dividing lines are added to separate two blood vessels. Through the correction instruction, the part misrecognized as other tissues can be modified into blood vessels, or the area misrecognized as blood vessels can be deleted, etc. After the processing module 12 receives the correction instruction, the display module 14 displays the vascular image data on the display interface, and at the same time displays a modification interface. The modification interface includes various modification tools (such as connection, deletion, filling, etc.). The processing module 12 can sequentially modify multiple areas of the vascular image data according to the modification tool selected by the user and the operations after selecting the modification tool. The processing module 12 can also modify multiple areas of the vascular image data simultaneously according to the shape or position distribution of the vascular image data when detecting the modification instruction, so that the size or shape of the blood vessels corresponding to the vascular data meets the preset requirements. The processing module 12 can use the corrected vascular image data as vascular data, or can further perform smoothing processing on the corrected vascular image data to obtain vascular data.

[0071] By modifying the vascular image data, the vascular image data can be made consistent with the actual vascular shape, thereby improving the display effect of the subsequent reconstructed image.

[0072] In another embodiment, the processing module 12 may use the vascular image data output by the vascular segmentation model as the vascular data.

[0073] In another embodiment, the processing module 12 may also determine the distribution position of the blood vessels based on the pixel values of each pixel point in the three-dimensional image data, and then extract the blood vessel data.

[0074] When the processing module 12 determines the slice thickness of the vascular data, the value of the first preset value can be adjusted according to the actual situation. For example, it can be 3 millimeters. The vascular data includes the coordinates and pixel values of multiple pixel points. The medical imaging processing device can determine whether the slice thickness of the vascular data is greater than the first preset value according to the spacing (spacing parameter) between two adjacent pixel points in the first direction in the vascular data. Here, the first direction is consistent with the slice thickness direction of the vascular data, that is, the first direction is perpendicular to each scanning plane. The spacing between two adjacent pixel points in the first direction can be the spacing between any two adjacent pixel points in the first direction, or the average value of multiple groups of spacings, and each group of spacings is the spacing between any two adjacent pixel points in the first direction.

[0075] In one embodiment, if the spacing between two adjacent pixel points in the first direction in the vascular data is greater than the second preset value, it is determined that the slice thickness of the vascular data is greater than the first preset value. Here, the value of the second preset value can be adjusted according to the actual situation. For example, it can be 3 millimeters.

[0076] In another embodiment, determine the spacing between two adjacent pixel points in the first direction in the vascular data, and use this spacing as the first spacing. Determine the spacing between two adjacent pixel points in the second direction, and use this spacing as the second spacing. Here, the second direction is perpendicular to the first direction. For example, if the first direction is the Z direction, the second direction can be any one of the X direction or the Y direction, and the second direction can also be the direction with the smallest spacing between two adjacent pixel points in the X direction and the Y direction. For example, calculate the spacing between two adjacent pixel points in the X direction and the spacing between two adjacent pixel points in the Y direction, and use the direction corresponding to the smallest spacing as the second direction.

[0077] After obtaining the first spacing and the second spacing, determine the ratio of the first spacing to the second spacing. If the ratio of the first spacing to the second spacing is greater than the preset ratio, it is determined that the slice thickness of the vascular data is greater than the first preset value. Exemplarily, the preset ratio is an integer. For example, it can be 2. After the medical imaging processing device obtains the ratio of the first spacing to the second spacing, round down this ratio, and determine whether the rounded-down ratio is greater than the preset ratio. If it is greater than the preset ratio, it is determined that the slice thickness of the vascular data is greater than the first preset value.

[0078] Judging whether the vascular data is thick-layer vascular data by the interval between pixel points can improve the calculation accuracy.

[0079] In one embodiment, the processing module 12 can also determine the specific value of the first preset value according to the image display accuracy input by the user. For example, when the image display accuracy is greater than the preset accuracy, the first preset value is smaller; when the image display accuracy is less than the preset accuracy, the first preset value is larger. The processing module 12 can also first determine the radius of the blood vessel and then determine the specific value of the first preset value according to the radius of the blood vessel. For example, when the radius of the blood vessel is small, a smaller first preset value is set.

[0080] When the slice thickness of the vascular data is greater than the first preset value, it indicates that the distance between the scanning surfaces corresponding to two adjacent two-dimensional image data is large, and the data volume of the vascular data is small, that is, the vascular data is thick-layer vascular data. The morphology of the vascular image reconstructed from the vascular data is poor. Then, the vascular data is thinned to obtain thinned vascular data, and the slice thickness of the thinned vascular data is less than or equal to the first preset value.

[0081] In one embodiment, the processing module 12 inputs the vascular data into a super-resolution model for processing to obtain thinned vascular data output by the super-resolution model. Specifically, the super-resolution model is obtained by training a classification model with multiple groups of training samples. Each group of training samples includes two vascular data with different resolutions. For example, for the same imaging device, different resolution parameters are set to scan the same object to obtain a group of vascular data with different resolutions. Or the high-resolution vascular data is downsampled to obtain low-resolution vascular data, and then a group of vascular data with different resolutions is obtained. The classification model can be a convolutional neural network model or a generative adversarial network model, or a model using an interpolation algorithm (such as bilinear interpolation). The classification model is trained with multiple groups of training samples to optimize the parameters of the classification model. The classification model with optimized parameters is the super-resolution model. The super-resolution model is used to perform interpolation processing on the input low-resolution image data and output high-resolution image data. The thinned vascular data is the vascular image data after interpolation processing, with more data volume, which is thin-layer vascular data and has a higher resolution than the vascular data.

[0082] Processing the thick-layer vascular data into thin-layer vascular data through the super-resolution model can better restore the vascular image without changing the original three-dimensional image data.

[0083] In another embodiment, the processing module 12 can also determine the shape and position distribution of the blood vessel according to the distribution of each pixel point in the vascular data, and perform interpolation processing on the vascular data according to the shape and position distribution of the blood vessel to obtain thinned vascular data.

[0084] In one embodiment, the superimposed display by the display module 14 based on the thinned vasculature data and the three-dimensional image data includes: extracting the points located on the center line from the thinned vasculature data, rendering the corresponding points as spheres respectively according to the radius corresponding to each point, and obtaining the vasculature data based on each sphere. Specifically, the thinned vasculature data is a set composed of multiple points. The display module 14 can determine the points located on the center line in the thinned vasculature data according to the coordinates of the multiple points that make up the thinned vasculature data. For any point located on the center line, determine the corresponding vasculature radius according to the position of the point and the distribution of the surrounding point set. According to the position and radius of each point, the corresponding point can be rendered as a sphere, so as to obtain a set composed of multiple spheres. The tubular structure formed by connecting multiple spheres together is the vasculature image.

[0085] Rendering the vasculature image according to the spheres corresponding to multiple points can make the structure of the obtained vasculature image closer to the real vasculature structure.

[0086] In another embodiment, the display module 14 can also perform image rendering according to the positions and pixel values of the pixel points in the thinned vasculature data to obtain the vasculature image.

[0087] In one embodiment, when the display module 14 determines that the slice thickness of the vasculature data is less than or equal to the first preset value, it reconstructs the vasculature image according to the vasculature data.

[0088] In one embodiment, the superimposed display by the display module 14 based on the thinned vasculature data and the three-dimensional image data includes: reconstructing the vasculature image according to the thinned vasculature data, reconstructing the tissue image according to the three-dimensional image data, and superimposing and displaying the vasculature image on the tissue image. For example, as Figure 2 shown, the display module 14 displays the tissue image 21 on the display interface and displays the vasculature image 22 on the tissue image 21, so that doctors can conveniently view the vasculature images of different parts.

[0089] In one embodiment, the superimposed display by the display module 14 based on the thinned vasculature data and the three-dimensional image data includes: reconstructing the vasculature image according to the thinned vasculature data, and superimposing and displaying the vasculature image on the image corresponding to the three-dimensional image data, so as to facilitate doctors to determine the position of the vasculature in the three-dimensional image.

[0090] In the above embodiments, by obtaining the vascular data in the three-dimensional image data, when the slice thickness of the vascular data is less than or equal to the first preset value, a vascular image is reconstructed according to the vascular data. When the slice thickness of the vascular data is greater than the first preset value, thinning processing is performed on the vascular data to obtain the thinned vascular data, and then a vascular image is reconstructed according to the thinned vascular data. Since the thinned vascular data is obtained by reducing the slice thickness of the vascular data, the thinned vascular data retains the original three-dimensional image data. Since the thinned vascular data has a smaller slice thickness and rich data, a vascular image with a better shape can be reconstructed without changing the original image.

[0091] An embodiment of the present application provides a surgical planning system, which includes a surgical planning module and the above-mentioned medical image processing system. The surgical planning module is used to plan a surgical path according to the vascular image. By using the above-mentioned medical image processing system, vascular data with higher clarity and accuracy can be obtained, thereby improving the accuracy of surgical planning.

[0092] An embodiment of the present application provides a medical image processing device, which can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server.

[0093] As Figure 3 shown, the medical image processing device includes a processor 31, a memory 32, and a computer program stored in the memory and executable on the processor.

[0094] As Figure 4 shown, when the processor executes the computer program, the following steps are implemented.

[0095] S401: Input three-dimensional image data of a target object.

[0096] S402: Obtain vascular data according to the three-dimensional image data.

[0097] S403: When the slice thickness of the vascular data is greater than the first preset value, perform thinning processing on the vascular data, and the slice thickness of the thinned vascular data is less than or equal to the first preset value.

[0098] S404: Perform overlay display according to the thinned vascular data and the three-dimensional image data.

[0099] It should be noted that the method implemented by the above computer program is the same as the functions of the corresponding modules in the above medical image processing system, and is based on the same concept as the above medical image processing system. For the specific implementation method and the technical effects brought, please refer to the embodiment part of the above medical image processing system, which will not be elaborated here.

[0100] In one embodiment, the implementation process of the medical image processing method executed by the processor 31 is as follows Figure 5 as shown. The processor reads CT or MR three-dimensional image data, and uses a vasculature segmentation model to segment vasculature data from the three-dimensional image data. Then, the processor determines whether the vasculature data is thick-layer vasculature data. If the vasculature data is not thick-layer vasculature data, a vasculature image is reconstructed based on the vasculature data. If the vasculature data is thick-layer vasculature data, a super-resolution module is used to thin the vasculature data to obtain thin-layer vasculature data, that is, the thinned vasculature data, and a vasculature image is reconstructed based on the thinned vasculature data. Regardless of whether the vasculature data is thin-layer vasculature data, a vasculature image can be reconstructed based on the thin-layer vasculature data, so that the morphology of the reconstructed vasculature image is better and closer to the real vasculature.

[0101] For example, after the hepatic vein data is segmented from the three-dimensional image data, the hepatic vein data is thick-layer vasculature data, and the vasculature image reconstructed directly based on the thick-layer vasculature data is as shown in Figure 6 as shown. The thin-layer vasculature data is generated based on the thick-layer vasculature data, and the vasculature image reconstructed based on the thin-layer vasculature data is as shown in Figure 7 as shown. After the hepatic portal vein data is segmented from the three-dimensional image data, the hepatic portal vein data is thick-layer vasculature data, and the vasculature image reconstructed directly based on the thick-layer vasculature data is as shown in Figure 8 as shown. The thin-layer vasculature data is generated based on the thick-layer vasculature data, and the vasculature image reconstructed based on the thin-layer vasculature data is as shown in Figure 9 as shown. It can be seen that when directly reconstructing the image based on the thick-layer vasculature data, there are steps on the outer wall of the vasculature in the obtained vasculature image. After generating the thin-layer vasculature data based on the thick-layer vasculature data and then reconstructing the image, a better vasculature morphology can be obtained.

[0102] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0103] Exemplarily, the computer program 33 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 32 and executed by the processor 31 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 33 in the medical image processing device.

[0104] Those skilled in the art can understand that Figure 3This is only an example of a medical image processing device and does not constitute a limitation on the medical image processing device. It may include more or fewer components than those shown, or combine certain components, or different components. For example, the medical image processing device may further include input / output devices, network access devices, buses, etc.

[0105] The processor 31 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0106] The memory 32 may be an internal storage unit of the medical image processing device, such as the hard disk or memory of the medical image processing device. The memory 32 may also be an external storage device of the medical image processing device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the medical image processing device. Further, the memory 32 may also include both the internal storage unit and the external storage device of the medical image processing device. The memory 32 is used to store the computer program and other programs and data required by the medical image processing device. The memory 32 may also be used to temporarily store data that has been output or is to be output.

[0107] An embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the above Figure 4 shown steps.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be repeated here.

[0109] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] In the embodiments provided in this application, it should be understood that the disclosed device / medical imaging processing device and method can be implemented in other ways. For example, the device / medical imaging processing device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0111] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, to implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0113] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0114] The above-described embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of this application, and should all be included in the protection scope of this application.

Claims

1. A medical image processing system, characterized in that, Comprising: An input module for inputting three-dimensional image data of a target object; A processing module for obtaining vascular data based on the three-dimensional image data; The processing module is further configured to, when the slice thickness of the vascular data is greater than a first preset value, perform thinning processing on the vascular data, and the slice thickness of the thinned vascular data is less than or equal to the first preset value; A display module for performing superimposed display according to the thinned vascular data and the three-dimensional image data.

2. The system according to claim 1, wherein Specifically, the processing module is further configured to: If the interval between two adjacent pixel points in the first direction in the vascular data is greater than a second preset value, it is determined that the slice thickness of the vascular data is greater than the first preset value, and the first direction is the same as the slice thickness direction of the vascular data.

3. The system according to claim 1, wherein Specifically, the processing module is further configured to: If the ratio of the first interval to the second interval is greater than a preset ratio, it is determined that the slice thickness of the vascular data is greater than the first preset value, where the first interval is the interval between two adjacent pixel points in the first direction in the vascular data, the second interval is the interval between the two adjacent pixel points in the second direction, the first direction is the same as the slice thickness direction of the vascular data, and the first direction is perpendicular to the second direction.

4. The system according to claim 1, wherein The processing module obtaining vascular data based on the three-dimensional image data includes: In response to the input of the three-dimensional image data, performing image segmentation on the three-dimensional image data to obtain vascular image data; Further, in response to a correction instruction for the vascular image data, correcting the vascular image data to obtain the vascular data.

5. The system according to claim 1, wherein The processing module performing thinning processing on the vascular data specifically includes: inputting the vascular data into a super-resolution model for processing to obtain thinned vascular data.

6. The system according to claim 1, wherein The display module performing superimposed display according to the thinned vascular data and the three-dimensional image data includes: Extracting the points located on the center line in the thinned vascular data; Rendering the corresponding points as spheres respectively according to the vascular radius corresponding to each point; Obtaining the vascular image according to each sphere.

7. The system according to any one of claims 1 to 6, characterized in that, The display module performing superimposed display according to the thinned vascular data and the three-dimensional image data includes: Reconstructing a vascular image according to the thinned vascular data; Reconstructing a tissue image according to the three-dimensional image data; Superimposing and displaying the vascular image on the tissue image; Or, Reconstructing a vascular image according to the thinned vascular data; Superimposing and displaying the vascular image on the image corresponding to the three-dimensional image data.

8. A surgical planning system, characterized in that, The surgical planning system includes a surgical planning module and the medical image processing system according to any one of claims 1 to 7, and the surgical planning module is configured to plan a surgical path according to the vascular image.

9. A medical image processing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it realizes: Inputting three-dimensional image data of a target object; Obtaining vascular data according to the three-dimensional image data; When the slice thickness of the vascular data is greater than a first preset value, performing thinning processing on the vascular data, and the slice thickness of the thinned vascular data is less than or equal to the first preset value; Performing superimposed display according to the thinned vascular data and the three-dimensional image data.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it realizes: Inputting three-dimensional image data of a target object; Obtaining vascular data according to the three-dimensional image data; When the slice thickness of the vascular data is greater than a first preset value, performing thinning processing on the vascular data so that the slice thickness of the thinned vascular data is less than or equal to the first preset value; Performing superimposed display according to the thinned vascular data and the three-dimensional image data.

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