CN114365190A - Spleen tumor identification method based on VRDS 4D medical image and related device - Google Patents
Spleen tumor identification method based on VRDS 4D medical image and related device Download PDFInfo
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Abstract
A spleen tumor identification method based on VRDS 4D medical images and a related device, the method comprises the following steps: acquiring a scan image (201) of a spleen of a target user; processing the scanned image of the spleen to obtain target medical image data, wherein the target medical image data comprises image data of the spleen and image data of blood vessels around the spleen (202); determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels (203); identifying (204) an abnormality type of the spleen based on the tumor characteristics of the spleen; 4D medical imaging is performed according to the target medical image data, and the abnormality type of the spleen is output (205). The method improves the accuracy and efficiency of spleen tumor identification of the medical imaging device.
Description
The application relates to the technical field of medical imaging devices, in particular to a spleen tumor identification method based on VRDS 4D medical images and a related device.
The spleen is the largest immune organ of the body, is positioned in the upper left abdomen, accounts for 25 percent of the total amount of the lymph tissue of the whole body, contains a large amount of lymphocytes and macrophages, and is the center of cellular immunity and humoral immunity of the body.
Currently, doctors still use the view and reading of continuous two-dimensional slice scan images, such as CT (computed tomography), MRI (magnetic resonance imaging), DTI (diffusion tensor imaging), PET (positron emission tomography) and the like, to determine and analyze spleen tumors of patients. However, the spleen is located under the diaphragm and protected by surrounding bones, so early symptoms of spleen tumors are not obvious and are not easy to be found, and the tumors cannot be identified sometimes only by directly watching two-dimensional slice data, so that the diagnosis of the spleen tumors by doctors is seriously influenced, and the treatment of the spleen tumors is delayed. With the rapid development of medical imaging technology, people put new demands on medical imaging.
Disclosure of Invention
The embodiment of the application provides a spleen tumor identification method based on VRDS 4D medical images and a related device, which are beneficial to improving the accuracy and efficiency of spleen tumor identification of a medical imaging device.
The embodiment of the application provides a spleen tumor identification method based on a VRDS 4D medical image in a first aspect, which comprises the following steps:
acquiring a scanned image of the spleen of a target user;
processing the scanned image of the spleen to obtain target medical image data, wherein the target medical image data comprises image data of the spleen and image data of blood vessels around the spleen;
determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels;
identifying an abnormal type of the spleen from the tumor characteristics of the spleen;
and performing 4D medical imaging according to the target medical image data, and outputting the abnormal type of the spleen.
A second aspect of embodiments of the present application provides a medical imaging apparatus, including:
an acquisition unit configured to acquire a scanned image of a spleen of a target user;
a processing unit, configured to process the scanned image of the spleen to obtain target medical image data, where the target medical image data includes image data of the spleen and image data of blood vessels around the spleen;
a determination unit for determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels;
an identification unit for identifying the spleen abnormal type according to the tumor characteristics of the spleen;
and the output unit is used for carrying out 4D medical imaging according to the target medical image data and outputting the abnormal type of the spleen.
A third aspect of embodiments of the present application provides a medical imaging apparatus comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and are generated as instructions to be executed by the processor to perform the steps of the method of any of the first aspects of the claims above.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium for storing a computer program, the stored computer program being executable by the processor to implement the method of any one of the above first aspects.
It can be seen that, in the above technical solution, a scanned image of the spleen of a target user is obtained, then the scanned image of the spleen is processed to obtain target medical image data, where the target medical image data includes image data of the spleen and image data of blood vessels around the spleen, next, a tumor characteristic of the spleen is determined according to the image data of the spleen and the image data of the blood vessels, next, an abnormal type of the spleen is identified according to the tumor characteristic of the spleen, and finally, 4D medical imaging is performed according to the target medical image data, and the abnormal type of the spleen is output. Therefore, the medical imaging device can identify the abnormal type of the spleen by processing the scanning image of the spleen and output the abnormal type of the spleen, so that the condition that the observation is not accurate enough based on human eyes is avoided, and the accuracy and the efficiency of spleen tumor identification performed by the medical imaging device are improved.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
fig. 1 is a schematic structural diagram of a VRDS 4D medical image-based intelligent analysis processing system according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a spleen tumor identification method based on VRDS 4D medical images according to an embodiment of the present application;
FIG. 3 is a schematic view of a medical imaging apparatus provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The following are detailed below.
The terms "first," "second," and "third" in the description and claims of this application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The medical imaging apparatus according to the embodiments of the present application refers to various apparatuses that reproduce the internal structure of a human body as an image using various media as information carriers, and the image information corresponds to the actual structure of the human body in terms of spatial and temporal distribution. The "DICOM data" refers to original image file data which reflects internal structural features of a human body and is acquired by medical equipment, and may include information such as computed tomography CT, magnetic resonance MRI, diffusion tensor imaging DTI, positron emission tomography PET-CT, and the "map source" refers to Texture2D/3D image volume data generated by analyzing the original DICOM data. "VRDS" refers to a Virtual Reality medical system (VRDS).
First, referring to fig. 1, fig. 1 is a schematic structural diagram of a VRDS 4D-based medical image intelligent analysis processing system 100 provided by an embodiment of the present application, where the system 100 includes a medical imaging apparatus 110 and a network database 120, where the medical imaging apparatus 110 may include a local medical imaging apparatus 111 and/or a terminal medical imaging apparatus 112, and the local medical imaging apparatus 111 or the terminal medical imaging apparatus 112 is configured to perform recognition, positioning, four-dimensional volume rendering, and anomaly analysis of a spleen tumor of a human body based on raw DICOM data based on a spleen tumor recognition method based on VRDS 4D medical images presented by the embodiment of the present application, so as to achieve a four-dimensional stereoscopic imaging effect (the 4-dimensional medical image specifically refers to a medical image including internal spatial structural features and external spatial structural features of a displayed tissue, the internal spatial structural features refer to slice data inside the tissue not lost, that is, the medical imaging apparatus may present the internal structure of the target organ, blood vessel, etc., and the external spatial structural characteristics refer to the environmental characteristics between tissues, including the spatial position characteristics (including intersection, spacing, fusion) between tissues, etc., such as the edge structural characteristics of the intersection position between spleen and artery, etc.), the local medical imaging apparatus 111 may also be used to edit the scanned image with respect to the terminal medical imaging apparatus 112, to form the transfer function result of the four-dimensional human body image, which may include the transfer function result of the internal organ surface of the human body and the tissue structure inside the internal organ of the human body, and the transfer function result of the cubic space, such as the number of sets of the cubic edit box and arc edit required by the transfer function, coordinates, color, transparency, etc. The network database 120 may be, for example, a cloud medical imaging apparatus, and the like, and the network database 120 is used to store the image sources generated by parsing the raw DICOM data and the transfer function results of the four-dimensional human body images edited by the local medical imaging apparatus 111, and the scanned images may be from a plurality of local medical imaging apparatuses 111 to realize interactive diagnosis of a plurality of doctors.
When the user performs specific image display by using the medical imaging apparatus 110, the user may select a display or a Head Mounted Display (HMDS) of the virtual reality VR to display in combination with an operation action, where the operation action refers to operation control performed on a four-dimensional human body image by the user through an external shooting device of the medical imaging apparatus, such as a mouse, a keyboard, a tablet computer (Pad), an ipad (internet portable device), and the like, so as to implement human-computer interaction, and the operation action includes at least one of the following: (1) changing the color and/or transparency of a specific organ/tissue, (2) positioning a zoom view, (3) rotating the view to realize multi-view 360-degree observation of a four-dimensional human body image, (4) entering the human body organ to observe an internal structure and perform real-time shearing effect rendering, and (5) moving the view up and down.
The following describes in detail a spleen tumor identification method based on VRDS 4D medical images according to an embodiment of the present application.
Referring to fig. 2 and fig. 2 are schematic flow charts of a spleen tumor identification method based on VRDS 4D medical images according to an embodiment of the present application, which is applied to the medical imaging apparatus shown in fig. 1, and as shown in fig. 2, the spleen tumor identification method based on VRDS 4D medical images according to the embodiment includes:
201. a scan image of the spleen of a target user is acquired.
Wherein the scanned image comprises any one of: CT images, MRI images, DTI images, PET-CT images.
In one possible example, the scan image further includes an enhanced scan image after contrast agent is driven.
202. Processing the scanned image of the spleen to obtain target medical image data, wherein the target medical image data comprises image data of the spleen and image data of blood vessels around the spleen.
In one possible example, processing the scan image of the spleen to obtain target medical image data comprises: generating a map source of the spleen according to the scanned image of the spleen; executing first preset processing aiming at a bitmap source to obtain a bitmap BMP data source; importing a BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises spleen image data and blood vessel image data; importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, wherein the second medical image data comprises spleen image data and blood vessel second image data, the blood vessel second image data comprises blood vessel surface features, and the blood vessel surface features are obtained by screening smooth muscle and elastic fiber image data in the blood vessel first image data through the cross blood vessel network model; and executing second preset processing aiming at the second medical image data to obtain target medical image data.
Optionally, generating a map source of the spleen from the scanned image of the spleen, comprising: the medical imaging device acquires a plurality of scanning images which are acquired by medical equipment and reflect the internal structure characteristics of the human body of a target user; screening at least one scanning image containing the spleen from a plurality of scanning images, and taking the at least one scanning image as medical digital imaging and communication DICOM data of a target user; parsing the DICOM data generates a map source of the target user, the map source including Texture2D/3D image volume data.
Optionally, the first preset processing includes at least one of the following operations: VRDS restriction contrast self-adaptive histogram equalization, mixed partial differential denoising and VRDS Ai elastic deformation processing.
Wherein the VRDS limited contrast adaptive histogram equalization comprises: region noise contrast amplitude limiting and global contrast amplitude limiting; dividing a local histogram of a graph source into a plurality of partitions, determining the inclination of a transformation function according to the inclination of a cumulative histogram of a neighborhood of each partition for each partition, determining the contrast amplification degree of the periphery of a pixel value of each partition according to the inclination of the transformation function, then carrying out limit cutting processing according to the contrast amplification degree to generate the distribution of an effective histogram and also generate the dereferencing value of an effective available neighborhood surrogate, and uniformly distributing the cut partial histograms to other areas of the histogram.
Wherein the mixed partial differential denoising comprises: different from Gaussian low-pass filtering (attenuation without distinction on high-frequency components of an image and image edge blurring generated during denoising), an isoluminance line (including an edge) formed by an object in a natural image is a curve which is smooth enough, namely the absolute value of the curvature of the isoluminance line is small enough, after the image is polluted by noise, the local gray value of the image is fluctuated randomly to cause the irregular oscillation of the isoluminance line, and the isoluminance line with large local curvature is formed.
Wherein, VRDS Ai elastic deformation processing comprises: on the original lattice, the positive and negative random distances are superposed to form a difference position matrix, then the gray scale on each difference position forms a new lattice, the distortion in the image can be realized, and in addition, the operations of rotation, distortion, translation and the like are carried out on the image.
As can be seen, in this example, the medical imaging apparatus obtains the BMP data source by processing the original scanned image data, so that the information amount of the original data is increased, the depth information is increased, and finally, the data meeting the display requirement of the 4D medical image is obtained.
Optionally, the first image data of the blood vessel includes fusion data of intersection positions of an artery and a vein, the second image data of the blood vessel includes surface features of the blood vessel, the surface features of the blood vessel are obtained by screening smooth muscle and elastic fiber image data in the first image data of the blood vessel through a cross blood vessel network model, the surface features of the blood vessel further include a data set of the artery and a data set of the vein, and the first data in the data set of the artery and the second data in the data set of the vein are independent of each other, the first data is data associated with the intersection positions, and the second data is data associated with the intersection positions.
The VRDS medical network model is provided with a transfer function of the structural characteristics of the spleen and a transfer function of the structural characteristics of blood vessels, the BMP data source obtains first medical image data through processing of the transfer functions, and the cross blood vessel network model realizes data separation of arteries and veins through the following operations: (1) extracting fusion data of the cross positions; (2) separating the fusion data based on a preset data separation algorithm aiming at each fusion data to obtain mutually independent artery boundary point data and vein boundary point data; (3) and integrating the multiple artery boundary point data obtained after processing into first data, and integrating the multiple vein boundary point data obtained after processing into second data.
Optionally, the second preset processing includes at least one of the following operations: 2D boundary optimization processing, 3D boundary optimization processing and data enhancement processing.
Wherein the 2D boundary optimization process comprises: and acquiring low-resolution information and high-resolution information through multiple sampling, wherein the low-resolution information can provide context semantic information of the segmented target in the whole image, namely characteristics reflecting the relation between the target and the environment, the characteristics are used for judging the object type, and the high-resolution information is used for providing more fine characteristics such as gradient and the like for the segmented target.
The 3D boundary optimization process includes: 3D convolution, 3D max pooling, and 3D up-convolution layers, the input data size is a1, a2, a3, the number of channels is c, the filter size is f, i.e., the filter dimension is f c, and the filter number is n, then the final output of the 3-dimensional convolution is:
(a1-f+1)*(a2-f+1)*(a3-f+1)*n
there are an analysis path and a synthesis path. In the analysis path, each layer contains two convolution kernels of 3 × 3, each followed by an activation function (Relu), and then a maximum pooling of 2 × 2 in each dimension merges two steps. In the synthesis path, each layer consists of 2 × 2 upward convolutions, with 2 steps in each dimension, followed by two 3 × 3 convolutions, and then Relu. The shortcut connections from the equal resolution layers in the analysis path then provide the basic high resolution features of the composite path. In the last layer, 1 × 1 convolution reduces the number of output channels.
Wherein, the data enhancement processing comprises any one of the following: data enhancement based on arbitrary angle rotation, data enhancement based on histogram equalization, data enhancement based on white balance, data enhancement based on mirroring operation, data enhancement based on random clipping, and data enhancement based on simulating different illumination variations.
In this example, the medical imaging device can process the BMP data source through the VRDS medical network model and the cross vascular network model, and obtain target image data by combining boundary optimization and data enhancement processing, so that the problems that the traditional medical image cannot realize integral separation of segmented arteries and veins and cannot extract the surface characteristics of blood vessels in the medical field are solved, and the authenticity, comprehensiveness and refinement degree of medical image display are improved.
203. Determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels.
In one possible example, determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels comprises: determining a tumor location of the spleen according to the image data of the spleen and the image data of the blood vessel; acquiring the characteristics of the spleen from the image data of the spleen, wherein the characteristics of the spleen comprise at least one piece of data of: the size of the spleen, the size of lymph nodes in the spleen, the degree of dilation of lymphatic vessels in the spleen; and determining the comprehensive expansion degree of the blood vessel according to the image data of the blood vessel.
Optionally, the image data of the blood vessel includes a vessel diameter and a vessel surface characteristic, and determining the comprehensive expansion degree of the blood vessel according to the image data of the blood vessel includes: determining the surface expansion degree of the blood vessel according to the surface characteristics of the blood vessel; acquiring physical parameters of a target user, wherein the physical parameters of the target user comprise at least one of the following: height, weight, blood pressure, blood glucose, heart rate of the target user; determining a first weight corresponding to the caliber of a blood vessel and a second weight corresponding to the surface expansion degree of the blood vessel according to the body parameters of the target user; and respectively carrying out weighting operation on the caliber of the blood vessel and the surface expansion degree of the blood vessel according to the first weight and the second weight to obtain the comprehensive expansion degree of the blood vessel.
Wherein, the caliber of the blood vessel can be obtained by the following modes: segmenting the blood vessel according to the first image data of the blood vessel to obtain M sections of blood vessels, wherein M is a positive integer; determining the average caliber of each blood vessel in M sections of blood vessels, wherein the caliber difference between the average caliber of the ith section of blood vessel and the average caliber of the (i +1) th section of blood vessel is not less than a preset segmentation threshold value, and i is a positive integer less than M; determining the weight corresponding to each of the M sections of blood vessels; and performing weighted operation on the average caliber of each section of blood vessel in the M sections of blood vessels according to the weight corresponding to each section of blood vessel in the M sections of blood vessels to obtain the caliber of the blood vessel.
Optionally, the determining the degree of surface dilation of the blood vessel according to the surface features of the blood vessel comprises: determining a target surface area of the blood vessel according to the surface features of the blood vessel, and acquiring first surface features of the target surface area of the blood vessel; acquiring a second surface feature of the target surface region of the normal blood vessel; the first surface features are compared to the second surface features to determine the degree of surface dilation of the blood vessel.
Wherein the determining a target surface region of the blood vessel from the surface features of the blood vessel comprises: analyzing the characteristic point distribution of the surface area of the blood vessel according to the surface characteristics of the blood vessel; intercepting a circular image of the surface area of the blood vessel according to N different circle centers to obtain N circular surface partitions, wherein N is an integer greater than 3; determining the number of feature points contained in each circular surface partition of the N circular surface partitions; and selecting a target circular surface partition from the N circular surface partitions, wherein the number of the characteristic points contained in the target circular surface partition is greater than the number of the characteristic points contained in other circular surface partitions in the N circular surface partitions.
It can be seen that in this example, by screening the target surface region of the blood vessel and comparing the surface features of the target surface region with those of the normal blood vessel, the complexity of feature comparison can be reduced, the comparison time can be shortened, and the comparison efficiency can be improved.
In one possible example, the image data of the blood vessel further includes a curvature of the blood vessel, and the method for determining the curvature of the blood vessel may be:
establishing a coordinate system according to second image data of the blood vessel, wherein the origin of the coordinate system is any position of the blood vessel, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule;
starting from the origin of the coordinate system, detecting along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to preset distances respectively, recording the spatial position corresponding to a first pixel point when detecting that a gray value corresponding to the first pixel point belongs to a gray value corresponding to the blood vessel cell data of the outermost layer of the blood vessel, and recording the spatial position corresponding to a second pixel point when detecting that a gray value corresponding to the second pixel point does not belong to a gray value corresponding to the blood vessel cell data of the outermost layer of the blood vessel and a gray value corresponding to an adjacent pixel point of the second pixel point belongs to a gray value corresponding to the blood vessel cell data of the outermost layer of the blood vessel;
segmenting the second image data of the blood vessel according to the spatial positions corresponding to all the first pixel points and the spatial positions corresponding to all the second pixel points to obtain a plurality of outermost blood vessel cell data sets corresponding to a plurality of blood vessels, wherein each outermost blood vessel cell data set comprises a plurality of outermost blood vessel cell data;
for each outermost vascular cell dataset, performing the steps of:
acquiring a characteristic curve of the outermost layer vascular cell data set projection in any plane; selecting any point of the characteristic curve as a starting point; starting from the starting point, marking pixel points continuously along the positive direction and the negative direction of the characteristic curve, stopping marking when a target pixel point is marked, wherein the positive direction of the characteristic curve is the transverse positive direction of second image data of the blood vessel, the negative direction of the characteristic curve is the transverse negative direction of the second image data of the blood vessel, the target pixel point is a pixel point with the largest curvature change of a target blood vessel section, the target blood vessel section is a blood vessel of the target blood vessel between the starting point and a target space position, the target blood vessel corresponds to an outermost layer blood vessel cell data set which is processed currently, and the target space position is a position corresponding to the target pixel point; obtaining the corresponding curvature of the target blood vessel section; and setting the curvature corresponding to the target blood vessel section as the corresponding curvature of the target blood vessel.
Based on the above example, the image data of the blood vessel includes a vessel diameter, a surface feature of the blood vessel, and a degree of curvature of the blood vessel, and determining the comprehensive degree of dilation of the blood vessel from the image data of the blood vessel includes: determining the surface expansion degree of the blood vessel according to the surface characteristics of the blood vessel; acquiring physical parameters of a target user, wherein the physical parameters of the target user comprise at least one of the following: height, weight, blood pressure, blood glucose, heart rate of the target user; determining a third weight corresponding to the caliber of the blood vessel, a fourth weight corresponding to the surface expansion degree of the blood vessel and a fifth weight corresponding to the bending degree of the blood vessel according to the body parameters of the target user; and respectively carrying out weighting operation on the caliber of the blood vessel, the surface expansion degree of the blood vessel and the bending degree of the blood vessel according to the third weight, the fourth weight and the fifth weight to obtain the comprehensive expansion degree of the blood vessel.
204. Identifying an abnormal type of the spleen based on the tumor characteristics of the spleen.
The spleen is an immune organ, blood circulation is rich, the incidence rate of tumors is low, more spleen benign tumors are common and most common hemangiomas, spleen malignant tumors are most common and most common, the spleen benign tumors comprise spleen cysts, hemangiomas, lymphangiomas, spleen hamartomas and the like, the spleen malignant tumors comprise hemangiosarcomas, lymphomas, metastatic tumors and the like, the spleen benign tumors are regular in general form, clear in boundary, complete in outline of the spleen outside a focus, and generally enlarged in spleen and lymph nodes.
The spleen cyst is a tumor-like cystic lesion of spleen tissues, can be clinically divided into parasitic cyst and non-parasitic cyst, has no blood vessel and has clear boundary;
hemangioma is the most common one of benign tumors of the spleen, the splenic hemangioma is the most common spongy hemangioma, and is divided into the spongy hemangioma and the capillary hemangioma according to the vasodilatation degree, and the spongy hemangioma and the capillary hemangioma can also exist in a mixed way, so the boundary is clear;
lymphangioma, which is composed of lymphatic sinuses with the pathological symptoms of dilatation, formed by obstruction and dilatation of lymphatic vessels, is divided into capillary lymphoma, cavernous lymphangioma and cystic lymphangioma, and has clear boundaries;
spleen hamartoma, including white marrow type spleen hamartoma, red marrow type spleen hamartoma and mixed type spleen hamartoma, wherein the white marrow type spleen hamartoma is composed of abnormal lymphoid tissues, the red marrow type spleen hamartoma is composed of disordered spleen sinuses, the mixed type spleen hamartoma has two types, and the boundary is clear;
lymphoma, a malignant tumor, is manifested by splenomegaly, aortic lymphadenectasis, severe splenomegaly, unclear boundaries, and low-medium reinforcement after intensive scanning;
angiosarcoma, malignant tumor, splenic tumor and splenic enlargement, lymphadenectasis, unclear boundary, and high-high reinforcement after enhancement scanning;
metastatic tumor, advanced manifestation of malignant tumor, enlarged lymph node, multiple metastasis simultaneously in liver, unclear boundary, and bovine eyeball sign or bulls-eye sign after enhancement scanning.
In one possible example, said identifying the abnormality type of the spleen from the tumor characteristics of the spleen comprises: when the size of the spleen does not exceed a first preset threshold and the size of the lymph node does not exceed a second preset threshold, determining the tumor boundary definition of the spleen according to the tumor position of the spleen; judging whether the tumor boundary definition of the spleen exceeds a preset definition threshold value or not; if the tumor boundary definition of the spleen exceeds a preset definition threshold, identifying the abnormal type of the spleen as the spleen cyst; if the tumor boundary definition of the spleen does not exceed a preset definition threshold, judging whether the comprehensive expansion degree of blood vessels exceeds a preset blood vessel expansion degree threshold, if the comprehensive expansion degree of the blood vessels exceeds the preset blood vessel expansion degree threshold, identifying that the abnormal type of the spleen is hemangioma, if the comprehensive expansion degree of the blood vessels does not exceed the preset blood vessel expansion degree threshold, judging whether the expansion degree of lymphatic vessels exceeds a preset lymphatic expansion degree threshold, and if the expansion degree of the lymphatic vessels exceeds the preset lymphatic expansion degree threshold, identifying that the abnormal type of the spleen is lymphangioma.
Specifically, when the size of the spleen is not more than the normal spleen size threshold and the size of the lymph node is not more than the normal lymph node size threshold, the spleen can be preliminarily judged to be benign tumor, the benign tumor of the spleen comprises spleen cyst, hemangioma, lymphangioma and the like, at this time, further judgment is needed, whether the spleen cyst is determined by judging the tumor boundary definition of the spleen, whether the hemangioma is determined by judging the comprehensive expansion degree of blood vessels, and whether the lymphangioma is determined by judging the expansion degree of the lymph vessels.
Optionally, the target medical image data further includes enhanced medical image data obtained by processing an enhanced scanned image of the spleen, and when the size of the spleen exceeds a first preset threshold or the size of a lymph node exceeds a second preset threshold, the tumor reinforcement degree of the spleen after enhanced scanning is determined according to the enhanced medical image data; judging whether the tumor strengthening degree of the spleen exceeds a preset strengthening degree threshold value or not; if the tumor strengthening degree of the spleen exceeds a preset strengthening degree threshold value, identifying the abnormal type of the spleen as angiosarcoma; and if the tumor strengthening degree of the spleen does not exceed a preset strengthening degree threshold value, identifying the abnormal type of the spleen as lymphoma.
Specifically, when the size of the spleen exceeds a normal spleen size threshold or the size of a lymph node exceeds a normal lymph node size threshold, the spleen can be preliminarily judged to be malignant tumor, the spleen malignant tumor comprises angiosarcoma, lymphoma, metastatic tumor and the like, and the abnormal type of the spleen can be further determined by judging the tumor strengthening degree of the spleen after the enhancement scanning. And if the tumor strengthening degree of the spleen exceeds a preset strengthening degree threshold value, identifying the abnormal type of the spleen as angiosarcoma, and if the tumor strengthening degree of the spleen does not exceed the preset strengthening degree threshold value, identifying the abnormal type of the spleen as lymphoma.
Optionally, whether the spleen tumor is a metastatic tumor can be judged by judging whether the spleen tumor after the enhanced scanning is of a bull's eye sign or a bull's-eye sign.
205. And performing 4D medical imaging according to the target medical image data, and outputting the abnormal type of the spleen.
Wherein, 4D medical imaging means presenting 4-dimensional medical images.
In one possible example, 4D medical imaging is performed from the target medical image data, including: the medical imaging device screens enhanced data with a quality score larger than a preset score from the target medical image data to serve as VRDS 4D imaging data; 4D medical imaging is performed from the VRDS 4D imaging data.
The quality score can be comprehensively evaluated from the following dimensions, such as average gradient, information entropy, visual information fidelity, peak signal-to-noise ratio PSNR, structural similarity SSIM, mean square error MSE, and the like, and a common image quality scoring algorithm in the image field can be specifically referred to, and is not repeated here.
It can be seen that, in the embodiment of the present application, a scanned image of the spleen of a target user is obtained, the scanned image of the spleen is processed to obtain target medical image data, where the target medical image data includes image data of the spleen and image data of blood vessels around the spleen, a tumor characteristic of the spleen is determined according to the image data of the spleen and the image data of the blood vessels, an abnormal type of the spleen is identified according to the tumor characteristic of the spleen, and finally, 4D medical imaging is performed according to the target medical image data, and the abnormal type of the spleen is output. Therefore, the medical imaging device can identify the abnormal type of the spleen by processing the scanning image of the spleen and output the abnormal type of the spleen, so that the condition that the observation is not accurate enough based on human eyes is avoided, and the accuracy and the efficiency of spleen tumor identification performed by the medical imaging device are improved.
Referring to fig. 3, fig. 3 is a schematic diagram of a medical imaging apparatus 300 according to an embodiment of the present application, where the medical imaging apparatus 300 may include:
an acquisition unit 301 configured to acquire a scanned image of the spleen of a target user;
a processing unit 302, configured to process the scanned image of the spleen to obtain target medical image data, where the target medical image data includes image data of the spleen and image data of blood vessels around the spleen;
a determining unit 303 for determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels;
an identification unit 304 for identifying the spleen abnormality type according to the tumor characteristics of the spleen;
an output unit 305, configured to perform 4D medical imaging according to the target medical image data, and output the abnormal type of the spleen.
In one possible example, the processing unit 302 is specifically configured to: generating a map source of the spleen from the scanned image of the spleen; executing first preset processing aiming at the graph source to obtain a bitmap BMP data source; importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises the image data of the spleen and the first image data of the blood vessel; importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, wherein the second medical image data comprises image data of the spleen and second image data of the blood vessel, the second image data of the blood vessel comprises surface features of the blood vessel, and the surface features of the blood vessel are obtained by screening smooth muscle and elastic fiber image data in the first image data of the blood vessel through the cross blood vessel network model; and executing second preset processing aiming at the second medical image data to obtain the target medical image data.
In one possible example, the determining unit 303 is specifically configured to: determining a tumor location of the spleen according to the image data of the spleen and the image data of the blood vessel; acquiring the characteristics of the spleen from the image data of the spleen, wherein the characteristics of the spleen comprise at least one piece of data of: the size of the spleen, the size of lymph nodes in the spleen, the degree of dilation of lymphatic vessels in the spleen; and determining the comprehensive expansion degree of the blood vessel according to the image data of the blood vessel.
In a possible example, the image data of the blood vessel includes a vessel diameter of the blood vessel and a surface feature of the blood vessel, and the determining unit 303 is specifically configured to: determining the surface expansion degree of the blood vessel according to the surface characteristics of the blood vessel; acquiring physical parameters of the target user, wherein the physical parameters of the target user comprise at least one of the following: height, weight, blood pressure, blood glucose, heart rate of the target user; determining a first weight corresponding to the caliber of the blood vessel and a second weight corresponding to the surface expansion degree of the blood vessel according to the body parameters of the target user; and respectively carrying out weighting operation on the caliber of the blood vessel and the surface expansion degree of the blood vessel according to the first weight and the second weight to obtain the comprehensive expansion degree of the blood vessel.
In one possible example, the medical imaging apparatus 300 further comprises a caliber obtaining unit 306, the caliber obtaining unit 306 is configured to: segmenting the blood vessel according to the first image data of the blood vessel to obtain M sections of blood vessels, wherein M is a positive integer; determining the average pipe diameter of each blood vessel in the M sections of blood vessels, wherein the pipe diameter difference between the average pipe diameter of the ith section of blood vessel and the average pipe diameter of the (i +1) th section of blood vessel is not less than a preset segmentation threshold value, and i is a positive integer less than M; determining a weight corresponding to each of the M sections of blood vessels; and carrying out weighted operation on the average caliber of each section of blood vessel in the M sections of blood vessels according to the corresponding weight of each section of blood vessel in the M sections of blood vessels to obtain the caliber of the blood vessel.
In one possible example, the determining unit 303 is specifically configured to: determining a target surface area of the blood vessel according to the surface features of the blood vessel, and acquiring first surface features of the target surface area of the blood vessel; acquiring a second surface feature of the target surface region of a normal blood vessel; comparing the first surface features to the second surface features to determine a degree of surface dilation of the blood vessel.
In one possible example, the determining unit 303 is specifically configured to: analyzing the characteristic point distribution of the surface area of the blood vessel according to the surface characteristics of the blood vessel; intercepting a circular image of the surface area of the blood vessel according to N different circle centers to obtain N circular surface partitions, wherein N is an integer greater than 3; determining the number of feature points contained in each circular surface partition of the N circular surface partitions; selecting a target circular surface partition from the N circular surface partitions, wherein the number of feature points included in the target circular surface partition is greater than the number of feature points included in other circular surface partitions of the N circular surface partitions.
In one possible example, the identifying unit 304 is specifically configured to: determining a tumor boundary definition of the spleen according to the tumor position of the spleen when the size of the spleen does not exceed a first preset threshold and the size of the lymph node does not exceed a second preset threshold; judging whether the tumor boundary definition of the spleen exceeds a preset definition threshold value or not; if the tumor boundary definition of the spleen exceeds the preset definition threshold, identifying the abnormal type of the spleen as a spleen cyst; if the tumor boundary definition of the spleen is not more than the preset definition threshold value, judging whether the comprehensive expansion degree of the blood vessels exceeds a preset blood vessel expansion degree threshold value or not, if the comprehensive expansion degree of the blood vessels exceeds the preset blood vessel expansion degree threshold value, identifying that the abnormal type of the spleen is hemangioma, if the comprehensive expansion degree of the blood vessels is not more than the preset blood vessel expansion degree threshold value, judging whether the expansion degree of the lymphatic vessels exceeds a preset lymphatic expansion degree threshold value or not, and if the expansion degree of the lymphatic vessels exceeds the preset lymphatic expansion degree threshold value, identifying that the abnormal type of the spleen is lymphangioma.
In a possible example, the target medical image data further comprises enhanced medical image data obtained by processing an enhanced scan image of the spleen, and the identification unit 304 is further configured to: when the size of the spleen exceeds the first preset threshold or the size of the lymph node exceeds the second preset threshold, determining the tumor reinforcement degree of the spleen after enhanced scanning according to the enhanced medical image data; judging whether the tumor strengthening degree of the spleen exceeds a preset strengthening degree threshold value or not; if the tumor reinforcement degree of the spleen exceeds the preset reinforcement degree threshold value, identifying the abnormal type of the spleen as angiosarcoma; and if the tumor reinforcement degree of the spleen does not exceed the preset reinforcement degree threshold value, identifying the abnormal type of the spleen as lymphoma.
For specific implementation of the medical imaging apparatus according to the present application, reference may be made to the above-mentioned embodiments of the spleen tumor identification method based on the VRDS 4D medical image, which is not described herein again.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application. As shown in fig. 4, a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application may include:
a processor 401, such as a CPU.
The memory 402 may alternatively be a high speed RAM memory or a stable memory such as a disk memory.
A communication interface 403 for implementing connection communication between the processor 401 and the memory 402.
Those skilled in the art will appreciate that the configuration of the medical imaging device shown in FIG. 4 is not intended to be limiting and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 4, an operating system, a network communication module, and programs for spleen tumor identification may be included in memory 402. The operating system is a program that manages and controls the hardware and software resources of the medical imaging device, a program that supports spleen tumor identification, and other software or program operations. The network communication module is used to enable communication between the components within the memory 402, as well as with other hardware and software within the medical imaging apparatus.
In the medical imaging apparatus shown in fig. 4, a processor 401 is used to execute a program for spleen tumor identification stored in a memory 402, implementing the following steps:
acquiring a scanned image of the spleen of a target user;
processing the scanned image of the spleen to obtain target medical image data, wherein the target medical image data comprises image data of the spleen and image data of blood vessels around the spleen;
determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels;
identifying an abnormal type of the spleen from the tumor characteristics of the spleen;
and performing 4D medical imaging according to the target medical image data, and outputting the abnormal type of the spleen.
For specific implementation of the medical imaging apparatus according to the present application, reference may be made to the above-mentioned embodiments of the spleen tumor identification method based on the VRDS 4D medical image, which is not described herein again.
The present application further provides a computer readable storage medium for storing a computer program, the stored computer program being executable by the processor to perform the steps of:
acquiring a scanned image of the spleen of a target user;
processing the scanned image of the spleen to obtain target medical image data, wherein the target medical image data comprises image data of the spleen and image data of blood vessels around the spleen;
determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels;
identifying an abnormal type of the spleen from the tumor characteristics of the spleen;
and performing 4D medical imaging according to the target medical image data, and outputting the abnormal type of the spleen.
For specific implementation of the computer-readable storage medium according to the present application, reference may be made to the above embodiments of the spleen tumor identification method based on VRDS 4D medical images, which are not described herein again.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art should understand that the present application is not limited by the order of acts described, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in this specification are preferred embodiments and that the acts and modules involved are not necessarily required for this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some interfaces, and may be in an electrical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a medical imaging apparatus, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (20)
- A spleen tumor identification method based on VRDS 4D medical images is applied to a medical imaging device, and comprises the following steps:acquiring a scanned image of the spleen of a target user;processing the scanned image of the spleen to obtain target medical image data, wherein the target medical image data comprises image data of the spleen and image data of blood vessels around the spleen;determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels;identifying an abnormal type of the spleen from the tumor characteristics of the spleen;and performing 4D medical imaging according to the target medical image data, and outputting the abnormal type of the spleen.
- The method of claim 1, wherein said processing the scan image of the spleen to obtain target medical image data comprises:generating a map source of the spleen from the scanned image of the spleen;executing first preset processing aiming at the graph source to obtain a bitmap BMP data source;importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises the image data of the spleen and the first image data of the blood vessel;importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, wherein the second medical image data comprises image data of the spleen and second image data of the blood vessel, the second image data of the blood vessel comprises surface features of the blood vessel, and the surface features of the blood vessel are obtained by screening smooth muscle and elastic fiber image data in the first image data of the blood vessel through the cross blood vessel network model;and executing second preset processing aiming at the second medical image data to obtain the target medical image data.
- The method of claim 2, wherein said determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels comprises:determining a tumor location of the spleen according to the image data of the spleen and the image data of the blood vessel;acquiring the characteristics of the spleen from the image data of the spleen, wherein the characteristics of the spleen comprise at least one piece of data of: the size of the spleen, the size of lymph nodes in the spleen, the degree of dilation of lymphatic vessels in the spleen;and determining the comprehensive expansion degree of the blood vessel according to the image data of the blood vessel.
- The method of claim 3, wherein the image data of the blood vessel includes a vessel diameter of the blood vessel and a surface feature of the blood vessel, and wherein determining the integrated degree of dilation of the blood vessel from the image data of the blood vessel comprises:determining the surface expansion degree of the blood vessel according to the surface characteristics of the blood vessel;acquiring physical parameters of the target user, wherein the physical parameters of the target user comprise at least one of the following: height, weight, blood pressure, blood glucose, heart rate of the target user;determining a first weight corresponding to the caliber of the blood vessel and a second weight corresponding to the surface expansion degree of the blood vessel according to the body parameters of the target user;and respectively carrying out weighting operation on the caliber of the blood vessel and the surface expansion degree of the blood vessel according to the first weight and the second weight to obtain the comprehensive expansion degree of the blood vessel.
- The method according to claim 4, wherein after importing the BMP data source into a predetermined VRDS medical network model to obtain first medical image data, the method further comprises:segmenting the blood vessel according to the first image data of the blood vessel to obtain M sections of blood vessels, wherein M is a positive integer;determining the average pipe diameter of each blood vessel in the M sections of blood vessels, wherein the pipe diameter difference between the average pipe diameter of the ith section of blood vessel and the average pipe diameter of the (i +1) th section of blood vessel is not less than a preset segmentation threshold value, and i is a positive integer less than M;determining a weight corresponding to each of the M sections of blood vessels;and carrying out weighted operation on the average caliber of each section of blood vessel in the M sections of blood vessels according to the corresponding weight of each section of blood vessel in the M sections of blood vessels to obtain the caliber of the blood vessel.
- The method of claim 4 or 5, wherein determining the degree of surface dilation of the blood vessel based on the surface features of the blood vessel comprises:determining a target surface area of the blood vessel according to the surface features of the blood vessel, and acquiring first surface features of the target surface area of the blood vessel;acquiring a second surface feature of the target surface region of a normal blood vessel;comparing the first surface features to the second surface features to determine a degree of surface dilation of the blood vessel.
- The method of claim 6, wherein the determining the target surface region of the blood vessel from the surface features of the blood vessel comprises:analyzing the characteristic point distribution of the surface area of the blood vessel according to the surface characteristics of the blood vessel;intercepting a circular image of the surface area of the blood vessel according to N different circle centers to obtain N circular surface partitions, wherein N is an integer greater than 3;determining the number of feature points contained in each circular surface partition of the N circular surface partitions;selecting a target circular surface partition from the N circular surface partitions, wherein the number of feature points included in the target circular surface partition is greater than the number of feature points included in other circular surface partitions of the N circular surface partitions.
- The method of claim 3, wherein said identifying said spleen abnormal type based on said spleen tumor characteristics comprises:determining a tumor boundary definition of the spleen according to the tumor position of the spleen when the size of the spleen does not exceed a first preset threshold and the size of the lymph node does not exceed a second preset threshold;judging whether the tumor boundary definition of the spleen exceeds a preset definition threshold value or not;if the tumor boundary definition of the spleen exceeds the preset definition threshold, identifying the abnormal type of the spleen as a spleen cyst;if the tumor boundary definition of the spleen is not more than the preset definition threshold value, judging whether the comprehensive expansion degree of the blood vessels exceeds a preset blood vessel expansion degree threshold value or not, if the comprehensive expansion degree of the blood vessels exceeds the preset blood vessel expansion degree threshold value, identifying that the abnormal type of the spleen is hemangioma, if the comprehensive expansion degree of the blood vessels is not more than the preset blood vessel expansion degree threshold value, judging whether the expansion degree of the lymphatic vessels exceeds a preset lymphatic expansion degree threshold value or not, and if the expansion degree of the lymphatic vessels exceeds the preset lymphatic expansion degree threshold value, identifying that the abnormal type of the spleen is lymphangioma.
- The method of claim 8, wherein the target medical image data further comprises enhanced medical image data processed from an enhanced scan image of the spleen, and wherein identifying the abnormality type of the spleen from the tumor characteristics of the spleen further comprises:when the size of the spleen exceeds the first preset threshold or the size of the lymph node exceeds the second preset threshold, determining the tumor reinforcement degree of the spleen after enhanced scanning according to the enhanced medical image data;judging whether the tumor strengthening degree of the spleen exceeds a preset strengthening degree threshold value or not;if the tumor reinforcement degree of the spleen exceeds the preset reinforcement degree threshold value, identifying the abnormal type of the spleen as angiosarcoma;and if the tumor reinforcement degree of the spleen does not exceed the preset reinforcement degree threshold value, identifying the abnormal type of the spleen as lymphoma.
- A medical imaging apparatus, characterized in that the apparatus comprises:an acquisition unit configured to acquire a scanned image of a spleen of a target user;a processing unit, configured to process the scanned image of the spleen to obtain target medical image data, where the target medical image data includes image data of the spleen and image data of blood vessels around the spleen;a determination unit for determining a tumor characteristic of the spleen from the image data of the spleen and the image data of the blood vessels;an identification unit for identifying the spleen abnormal type according to the tumor characteristics of the spleen;and the output unit is used for carrying out 4D medical imaging according to the target medical image data and outputting the abnormal type of the spleen.
- The apparatus according to claim 10, wherein the processing unit is specifically configured to:generating a map source of the spleen from the scanned image of the spleen;executing first preset processing aiming at the graph source to obtain a bitmap BMP data source;importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises the image data of the spleen and the first image data of the blood vessel;importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, wherein the second medical image data comprises image data of the spleen and second image data of the blood vessel, the second image data of the blood vessel comprises surface features of the blood vessel, and the surface features of the blood vessel are obtained by screening smooth muscle and elastic fiber image data in the first image data of the blood vessel through the cross blood vessel network model;and executing second preset processing aiming at the second medical image data to obtain the target medical image data.
- The apparatus according to claim 11, wherein the determining unit is specifically configured to:determining a tumor location of the spleen according to the image data of the spleen and the image data of the blood vessel;acquiring the characteristics of the spleen from the image data of the spleen, wherein the characteristics of the spleen comprise at least one piece of data of: the size of the spleen, the size of lymph nodes in the spleen, the degree of dilation of lymphatic vessels in the spleen;and determining the comprehensive expansion degree of the blood vessel according to the image data of the blood vessel.
- The apparatus according to claim 12, wherein the image data of the blood vessel comprises a vessel diameter of the blood vessel and a surface feature of the blood vessel, and the determining unit is specifically configured to:determining the surface expansion degree of the blood vessel according to the surface characteristics of the blood vessel;acquiring physical parameters of the target user, wherein the physical parameters of the target user comprise at least one of the following: height, weight, blood pressure, blood glucose, heart rate of the target user;determining a first weight corresponding to the caliber of the blood vessel and a second weight corresponding to the surface expansion degree of the blood vessel according to the body parameters of the target user;and respectively carrying out weighting operation on the caliber of the blood vessel and the surface expansion degree of the blood vessel according to the first weight and the second weight to obtain the comprehensive expansion degree of the blood vessel.
- The apparatus according to claim 13, further comprising a pipe diameter obtaining unit for:segmenting the blood vessel according to the first image data of the blood vessel to obtain M sections of blood vessels, wherein M is a positive integer;determining the average pipe diameter of each blood vessel in the M sections of blood vessels, wherein the pipe diameter difference between the average pipe diameter of the ith section of blood vessel and the average pipe diameter of the (i +1) th section of blood vessel is not less than a preset segmentation threshold value, and i is a positive integer less than M;determining a weight corresponding to each of the M sections of blood vessels;and carrying out weighted operation on the average caliber of each section of blood vessel in the M sections of blood vessels according to the corresponding weight of each section of blood vessel in the M sections of blood vessels to obtain the caliber of the blood vessel.
- The apparatus according to claim 13 or 14, wherein the determining unit is specifically configured to:determining a target surface area of the blood vessel according to the surface features of the blood vessel, and acquiring first surface features of the target surface area of the blood vessel;acquiring a second surface feature of the target surface region of a normal blood vessel;comparing the first surface features to the second surface features to determine a degree of surface dilation of the blood vessel.
- The apparatus according to claim 15, wherein the determining unit is specifically configured to:analyzing the characteristic point distribution of the surface area of the blood vessel according to the surface characteristics of the blood vessel;intercepting a circular image of the surface area of the blood vessel according to N different circle centers to obtain N circular surface partitions, wherein N is an integer greater than 3;determining the number of feature points contained in each circular surface partition of the N circular surface partitions;selecting a target circular surface partition from the N circular surface partitions, wherein the number of feature points included in the target circular surface partition is greater than the number of feature points included in other circular surface partitions of the N circular surface partitions.
- The apparatus according to claim 12, wherein the identification unit is specifically configured to:determining a tumor boundary definition of the spleen according to the tumor position of the spleen when the size of the spleen does not exceed a first preset threshold and the size of the lymph node does not exceed a second preset threshold;judging whether the tumor boundary definition of the spleen exceeds a preset definition threshold value or not;if the tumor boundary definition of the spleen exceeds the preset definition threshold, identifying the abnormal type of the spleen as a spleen cyst;if the tumor boundary definition of the spleen is not more than the preset definition threshold value, judging whether the comprehensive expansion degree of the blood vessels exceeds a preset blood vessel expansion degree threshold value or not, if the comprehensive expansion degree of the blood vessels exceeds the preset blood vessel expansion degree threshold value, identifying that the abnormal type of the spleen is hemangioma, if the comprehensive expansion degree of the blood vessels is not more than the preset blood vessel expansion degree threshold value, judging whether the expansion degree of the lymphatic vessels exceeds a preset lymphatic expansion degree threshold value or not, and if the expansion degree of the lymphatic vessels exceeds the preset lymphatic expansion degree threshold value, identifying that the abnormal type of the spleen is lymphangioma.
- The apparatus as recited in claim 17, wherein the target medical image data further comprises enhanced medical image data resulting from processing an enhanced scan image of the spleen, the identification unit further configured to:when the size of the spleen exceeds the first preset threshold or the size of the lymph node exceeds the second preset threshold, determining the tumor reinforcement degree of the spleen after enhanced scanning according to the enhanced medical image data;judging whether the tumor strengthening degree of the spleen exceeds a preset strengthening degree threshold value or not;if the tumor reinforcement degree of the spleen exceeds the preset reinforcement degree threshold value, identifying the abnormal type of the spleen as angiosarcoma;and if the tumor reinforcement degree of the spleen does not exceed the preset reinforcement degree threshold value, identifying the abnormal type of the spleen as lymphoma.
- A medical imaging apparatus comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and generated as instructions for execution by the processor to perform the steps of the method of any of claims 1-9.
- A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, which is executed by the processor, to implement the method of any of claims 1-9.
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