CN114401673A - Stomach tumor identification method based on VRDS 4D medical image and related product - Google Patents
Stomach tumor identification method based on VRDS 4D medical image and related product Download PDFInfo
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Abstract
A gastric tumor identification method based on VRDS 4D medical images and a related product are applied to a medical imaging device, and the method comprises the following steps: the method comprises the steps of obtaining a stomach scanning image (201) of a target object through a VRDS 4D imaging technology, processing the stomach scanning image to obtain stomach 4D image data (202) of the target object, extracting stomach wall 4D image data (203) located on a stomach wall from the stomach 4D image data, determining a target position (204) of a tumor according to the stomach wall 4D image data, and determining a stomach tumor type (205) corresponding to the target object according to the target position of the tumor, so that the position of the tumor can be accurately positioned based on the 4D image data, and the accuracy of judging the stomach tumor type is improved.
Description
The application relates to the technical field of medical imaging devices, in particular to a gastric tumor identification method based on VRDS 4D medical images and a related product.
Currently, doctors still use the view of continuous two-dimensional slice scan images, such as CT (computed tomography), MRI (magnetic resonance imaging), DTI (diffusion tensor imaging), PET (positron emission tomography), etc., to judge and analyze the pathological tissues, such as tumors, of patients. However, the specific location of the tumor cannot be determined by simply looking directly at the two-dimensional slice data, which seriously affects the diagnosis of the disease by the physician. 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 gastric tumor identification method based on VRDS 4D medical images and a related product, which are beneficial to improving the efficiency of disease analysis.
In a first aspect, an embodiment of the present application provides a method for identifying a gastric tumor based on a VRDS 4D medical image, applied to a medical imaging apparatus, including:
acquiring a stomach scanning image of a target object by a VRDS 4D imaging technology;
processing the stomach scanning image to obtain stomach 4D image data of the target object;
extracting 4D image data including a stomach wall located at the stomach wall from the stomach 4D image data;
determining a target position of a tumor according to the 4D image data of the stomach wall;
and determining the stomach tumor type corresponding to the target object according to the target position of the tumor.
In a second aspect, the present application provides a gastric tumor identification apparatus based on VRDS 4D medical images, applied to a medical imaging apparatus, the apparatus including:
the acquisition unit is used for acquiring a stomach scanning image of the target object by a VRDS 4D imaging technology;
the processing unit is used for processing the stomach scanning image to obtain stomach 4D image data of the target object;
an extraction unit for extracting stomach wall 4D image data located on a stomach wall from the stomach 4D image data;
a determination unit for determining a target position of a tumor from the 4D image data of the stomach wall;
the determining unit is further configured to determine a gastric tumor type corresponding to the target object according to the target position of the tumor.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
Reference will now be made in brief to the drawings that are needed in describing embodiments or prior art.
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.
Fig. 1 is a schematic structural diagram of a condition analysis processing system 100 based on VRDS 4D according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a gastric tumor identification method based on VRDS 4D medical images according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a medical imaging apparatus provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an embodiment of a gastric tumor identification device based on VRDS 4D medical images according to an embodiment of the present application.
In order to make the technical solutions of the present application better understood, 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 terms "first," "second," and the like in the description and claims of the present 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. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
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).
Referring to fig. 1, a schematic structural diagram of a system 100 for identifying and processing gastric neoplasia based on VRDS 4D medical images provided by an embodiment of the present application is shown, the system 100 includes a medical imaging device 110 and a network database 120, wherein the medical imaging device 110 may include a local medical imaging device 111 and/or a terminal medical imaging device 112, the local medical imaging device 111 or the terminal medical imaging device 112 is configured to perform stomach identification, positioning, four-dimensional volume rendering, and anomaly analysis based on raw DICOM data and based on a gastric neoplasia identification algorithm of the VRDS 4D medical images presented by an 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 that slice data inside the tissue is not lost, that is, the medical imaging device may present the internal structure of tissues such as stomach and blood vessel, and the external spatial structural characteristics refer to the environmental characteristics between tissues, including the spatial position characteristics (including intersection, spacing, fusion) between tissues, and the like, the edge structural characteristics of the intersection position between organs such as stomach and blood vessel, and the like), the local medical imaging device 111 may also be used to edit the image source data with respect to the terminal medical imaging device 112, to form the transfer function result of the four-dimensional human body image, which may include the transfer function result of the tissue structure on the surface of stomach and in stomach, and the transfer function result of the cubic space, such as the number of sets of cubic edit boxes and arc edits required by the transfer function, coordinates, colors, transparency, and the like. The network database 120 may be, for example, a cloud server, and the like, and the network database 120 is configured to store a map source generated by parsing the raw DICOM data and a transfer function result of the four-dimensional human body image edited by the local medical imaging apparatus 111, where the map source may be from a plurality of local medical imaging apparatuses 111 to implement 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 interior of the stomach to observe internal structures, and performing real-time shearing effect rendering, and (5) moving the view up and down.
The following describes in detail a gastric tumor identification method based on VRDS 4D medical images according to an embodiment of the present application.
Please refer to fig. 2, which is a flowchart illustrating an embodiment of a method for gastric tumor identification based on VRDS 4D medical images according to the present application. The gastric tumor identification method based on VRDS 4D medical images described in the embodiment comprises the following steps:
201. and acquiring a stomach scanning image of the target object by a VRDS 4D imaging technology.
Wherein, the target object can be any user or patient, and the stomach scanning image can include any one of the following: CT images, MRI images, DTI images, PET-CT images, etc., without limitation. The medical imaging device may acquire a stomach scan image reflecting internal structures of a stomach of the target subject.
202. And processing the stomach scanning image to obtain the stomach 4D image data of the target object.
A VRDS (Virtual Reality vector system) system may be input for the stomach scan image acquired by the medical imaging device to obtain stomach 4D image data of the target object, where the stomach 4D image data includes internal and external spatial structural features of the stomach.
Optionally, in step 202, processing the stomach scan image to obtain stomach 4D image data of the target object, includes:
21. performing first preset processing on the stomach scanning image to obtain a bitmap BMP data source;
22. 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 a stomach data set and a blood vessel data set, and the stomach data set comprises a gastric mucosa data set;
23. 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 a blood vessel data set;
24. executing a second preset process on the second medical image data to obtain the target 4D image, wherein the 4D image data includes: stomach 4D image data and a blood vessel data set.
Wherein, the first preset process may include at least one of the following operations: VRDS-limited contrast adaptive histogram equalization, hybrid partial differential de-noising, VRDS Ai elastic deformation processing, and the like, without limitation; a VRDS medical network model can be preset in the medical imaging device, the medical imaging device obtains a BMP data source by processing stomach scanning image data, the information amount of original data is improved, depth dimension information is increased, and data meeting the display requirement of 4D medical images are finally obtained.
In addition, the medical imaging device leads the BMP data source into a preset VRDS medical network model, each transfer function in a prestored transfer function set can be called through the VRDS medical network model, the BMP data source is processed through a plurality of transfer functions in the transfer function set, and first medical image data are obtained, wherein the transfer function set can comprise a blood vessel transfer function and a stomach transfer function which are preset through a reverse editor, and therefore the first medical image data are obtained through the preset VRDS medical network model, and accuracy and efficiency of obtaining the data can be improved.
Furthermore, a cross blood vessel network model can be preset in the medical imaging device, the preset cross blood vessel network model can be a trained neural network model, the first medical image data can be imported into the preset cross blood vessel network model, data segmentation can be carried out through the cross blood vessel network model, a data set blood vessel data set of the stomach is obtained, the blood vessel data set comprises data related to the cross position of the blood vessel, and finally, second medical image data can be obtained, so that data segmentation between the data corresponding to the blood vessel and the data corresponding to the stomach can be achieved through the cross blood vessel network model.
Further, the second preset processing includes at least one of the following methods: 2D boundary optimization processing, 3D boundary optimization processing, data enhancement processing, and the like, which are not limited herein; the 2D boundary optimization process includes: and acquiring low-resolution information and high-resolution information by 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 segmented target and the environment, the characteristics are used for judging the object type, and the high-resolution information is used for providing more precise characteristics such as gradient and the like for the segmented target, wherein the segmented target can comprise the stomach and the blood vessel, so that the second medical image data is processed to obtain a target 4D image, and the target 4D image can comprise the stomach 4D image data and the blood vessel data set.
203. Stomach wall 4D image data located on a stomach wall is extracted from the stomach 4D image data.
Since stomach tumors are usually frequently found in the stomach wall, the 4D image data of the stomach wall can be extracted from the 4D image data of the stomach, and the 4D image data of the stomach wall can include: the internal spatial structure characteristics of the stomach wall, as well as the external spatial structure characteristics, may also present the internal structure of tissues such as the stomach wall and blood vessels.
204. And determining the target position of the tumor according to the 4D image data of the stomach wall.
Among them, stomach tumors are mostly found in the mucosal layer, submucosa, muscularis layer or the outer wall of the stomach under the stomach wall, and the stomach tissue is thick, so that the target position of the tumor can be determined by the internal space structural characteristics and the external space structural characteristics of the stomach wall.
Optionally, in step 204, determining the target position of the tumor according to the 4D image data of the stomach wall may include the following steps:
41. determining mucosa layer image data, submucosa layer image data, muscularis layer image data and stomach wall image data corresponding to the stomach wall of the target object according to the 4D stomach wall image data;
42. determining abnormal data corresponding to the stomach wall according to the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data;
43. and determining the target position corresponding to the tumor according to the abnormal data.
Wherein, since the tumor of the stomach wall is limited to the structure of the stomach wall, in order to determine the target location corresponding to the tumor, the internal and external spatial structural features of the mucosa, the internal and external spatial structural features of the submucosa, the internal and external spatial structural features of the muscularis and the internal and external spatial structural features of the stomach wall corresponding to the stomach wall can be determined and processed to determine abnormal data in the spatial structural features, for example, if the difference between the internal and external spatial structural features and the normal features corresponding to the mucosa is too large, the target location corresponding to the tumor can be determined to be the mucosa layer; therefore, the position of the tumor can be accurately positioned through the 4D image data of the stomach wall, and the operation such as gastroscope is not needed, so that the examination experience of the target object is improved.
Optionally, in step 41, determining mucosa layer image data, submucosa layer image data, muscularis layer image data and extramural wall image data corresponding to the stomach wall of the target object according to the 4D stomach wall image data may include the following steps:
411. according to the 4D image data of the stomach wall, determining the space coordinate of each data in the 4D image data of the stomach wall to obtain a plurality of space coordinates;
412. according to the plurality of space coordinates, determining a plurality of target space coordinates corresponding to the stomach wall corresponding to the target object, wherein the stomach wall comprises at least one of the following from inside to outside: mucosal layer, submucosa, muscularis layer and stomach wall;
413. determining a mucosa layer space coordinate set, a submucosa layer space coordinate set, a muscularis layer space coordinate set and a stomach outer wall space coordinate set according to a plurality of target space coordinates corresponding to the stomach wall;
414. and determining the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data corresponding to the target object according to the mucosa layer space coordinate set, the submucosa layer space coordinate set, the muscularis layer space coordinate set and the stomach outer wall space coordinate set.
Wherein, the stomach wall 4D image data reflects spatial structure characteristics of each tissue structure of the stomach wall portion, so that a target position of a tumor of a target object can be determined by the spatial structure characteristics, specifically, a plurality of data corresponding to each tissue of the stomach wall portion can be determined, a plurality of spatial coordinates corresponding to the plurality of tissues can be obtained, each tissue can correspond to a plurality of spatial coordinates, since the stomach wall 4D image data is the image data of each tissue, in order to improve accuracy, a plurality of target spatial coordinates corresponding to a mucosa layer, a submucosa layer, a muscularis layer and a stomach outer wall can be determined, and then, in order to specifically locate the target position of the tumor, classification of the spatial coordinates can be performed on each stomach wall layer, so as to obtain a mucosa layer spatial coordinate set, a submucosa layer spatial coordinate set, a muscularis layer spatial coordinate set and a stomach outer wall spatial coordinate set, finally, mucosa layer image data, submucosal layer image data, muscularis layer image data and stomach wall image data which respectively correspond to the space coordinate set can be obtained from the stomach wall 4D image data, and therefore the accuracy of subsequently determining the tumor position can be improved.
Optionally, in step 42, determining abnormal data corresponding to the stomach wall according to the mucosal layer image data, the submucosal layer image data, the muscularis layer image data, and the stomach outer wall image data may include the following data:
421. according to the mucosa layer image data, the submucosal layer image data, the muscularis image data and the stomach outer wall image data, mucosa layer characteristic data, submucosal layer characteristic data, muscularis and stomach outer wall characteristic data which respectively correspond to the mucosa layer, the submucosal layer, the muscularis and the stomach outer wall are generated, and the characteristic data comprise at least one of the following data: spatial position, height, thickness, diameter and shape;
422. matching the mucosa characteristic data, the submucosal layer characteristic data and the stomach outer wall characteristic data with a preset normal data set corresponding to the stomach wall to obtain a plurality of matching values, wherein the preset stomach wall normal characteristic data set comprises at least one of the following data: mucosal layer standard characteristic data, submucosal layer standard characteristic data and stomach external wall standard characteristic data
423. And selecting characteristic data corresponding to the matching value smaller than or equal to a preset threshold value from the plurality of matching values as abnormal data corresponding to the stomach wall.
The preset threshold may be set by the user or default to the system, after a lesion occurs in the stomach, a tumor may be generated, and most of the tumor is in a raised form or is a polyp, which may cause swelling or sinking of different parts or tissues of the stomach wall, so that feature data corresponding to different parts may be generated according to internal and external spatial structural features respectively corresponding to a mucosal layer, a submucosal layer, a muscular layer, and a stomach outer wall of the stomach wall, where the feature data may include at least one of the following: spatial location, height, thickness, diameter, length, shape, etc., and are not limited thereto.
In addition, the medical imaging apparatus may pre-store a preset normal data set under normal conditions corresponding to the mucosal layer, the submucosal layer, the muscularis layer, and the stomach outer wall, respectively, to perform matching, so as to obtain a plurality of matching values, and if the matching values are less than or equal to the preset threshold, the matching may be considered as failed, so that the feature data corresponding to the matching values less than or equal to the preset threshold may be selected as the abnormal data corresponding to the stomach, that is, the data may be corresponding to the tumor region.
Optionally, in step 43, the determining the target position corresponding to the tumor according to the abnormal data may include the following steps:
431. screening a plurality of spatial position data corresponding to the abnormal data;
432. traversing parts corresponding to the plurality of spatial position data to obtain a plurality of target spatial position data corresponding to the tumor;
433. and positioning the tumor according to the plurality of target space position data to obtain the target position corresponding to the tumor.
In order to determine a specific position corresponding to a tumor region, the tumor region may be located by a plurality of spatial position data, and the abnormal data may include at least one of: the spatial position, height, thickness, diameter, length, shape, etc. may be, without limitation, a plurality of portions or tissues corresponding to the abnormal data, so that a plurality of spatial position data corresponding to the abnormal data may be screened from a plurality of feature data of the abnormal data, and the plurality of spatial position data may be traversed to determine a plurality of target spatial position data corresponding to the tumor, and the tumor may be located according to the plurality of target spatial position data to obtain a specific position of the tumor.
Optionally, in the step 432, the traversing the parts corresponding to the plurality of spatial position data to obtain a plurality of target spatial position data corresponding to the tumor may include the following steps:
4321. determining a three-dimensional space region corresponding to the plurality of spatial position data according to the plurality of spatial position data;
4322. dividing the three-dimensional space region into K layers of subspaces from top to bottom, wherein K is a positive integer greater than 1;
4323. and traversing the K-layer subspace to obtain an L-layer target subspace corresponding to the tumor, wherein a plurality of spatial position data corresponding to the L-layer target subspace are the target spatial position data, and L is a positive integer less than or equal to K.
Wherein, because the stomach tissue is thick and is difficult to locate the specific position of the tumor, the three-dimensional space region corresponding to the plurality of spatial position data corresponding to the plurality of tissue parts can be located through the spatial position region, in addition, because the tumor can grow longitudinally relative to the stomach, the three-dimensional space region can be divided into K layers of subspaces from top to bottom, each layer of subspaces can correspond to a layer of tissue structure in the stomach, the spatial position data of each layer of the K layers of subspaces can be traversed to obtain L layers of target subspaces in an abnormal state, the plurality of spatial position data corresponding to the L layers of target subspaces are a plurality of target spatial position data, thus, the spatial layer number corresponding to the tumor can be accurately located for each layer of texture, and the position corresponding to the spatial layer number is favorable for improving the positioning accuracy.
205. And determining the stomach tumor type corresponding to the target object according to the target position of the tumor.
After the tumor is located, namely after the target position of the tumor is determined, the type of the tumor can be determined according to the abnormal data corresponding to the target position, and the type of the tumor can be divided into at least one of the following types: early stage tumors and progressive stage tumors, not limited herein; the type of the tumor can be determined based on the characteristics of the tumor, such as growth position, infiltration depth, diameter and shape.
Optionally, in step 205, the determining the gastric tumor type corresponding to the target object according to the target location of the tumor may include the following steps:
51. determining the depth of the tumor infiltrated relative to the stomach wall and morphological characteristics of the tumor according to the abnormal data corresponding to the target position of the tumor;
52. determining the type of the tumor according to the target position of the tumor, the depth and the morphological characteristics.
Wherein, the types of the tumors can be divided into at least one of the following types: early stage tumors and advanced stage tumors, not limited herein; the type of the tumor may be determined based on characteristics such as a growth position, an infiltration depth, a diameter, and a form of the tumor, and specifically, the type of the tumor may be determined by determining aspects such as a depth of the tumor infiltrated into the stomach wall and a form characteristic of the tumor from the abnormality data.
For example, if the tumor is located in the mucosal layer or submucosal layer, it can be determined which tumor is the tumor by the diameter of the tumor, the early tumor is generally located in the mucosal layer or submucosal layer, the tumor of this type is generally referred to as a micro tumor with a lesion diameter of less than 0.5 cm, and is referred to as a small tumor with a lesion diameter of 0.6 to 1.0 cm, and therefore, for example, the state of the tumor can be determined from the form or height of the tumor, some tumors are in a raised state and can appear as polyps, in which case the form of the tumor can be raised, and if the tumor is micro-raised, the height of the tumor is less than 2 times the thickness of the submucosal layer, and therefore, the type of the tumor can be determined by various morphological features or infiltration depth of the tumor.
In a possible example, if the stomach 4D image data includes a blood vessel data set, the method further includes the following steps:
a1, classifying the blood vessel data sets to obtain peripheral blood vessel data sets and peripheral cross blood vessel data sets;
a2, if the target position of the tumor infiltrates into the outer wall of the stomach, determining the position incidence relation between the tumor and peripheral blood vessels according to a peripheral blood vessel data set and a peripheral cross blood vessel data set;
a3, if the position association relationship is close, determining that the tumor has metastasis.
Wherein, if the tumor lesion is serious, the infiltration depth exceeds the stomach outer wall at the outermost layer of the stomach wall, the tumor may be transferred to the lymph and other parts, after the tumor grows out of the stomach, the tumor can be observed better than in the stomach, at this time, whether the tumor is transferred or not can be determined by the blood vessel data connected with the stomach, the internal space structure characteristic corresponding to the stomach 4D image data can present the internal structure of the tissues such as the stomach and the blood vessel, the external space structure characteristic can present the environment characteristic between the tissues, including the space position characteristic (including crossing, spacing, fusion) between the tissues, and the like, therefore, the blood vessel data set and the peripheral cross blood vessel data set at the periphery of the stomach can be obtained by the blood vessel data set, when the infiltration depth of the tumor exceeds the stomach outer wall, the peripheral blood vessel tissue can be changed, and the position relationship between the tumor and the peripheral blood vessel can be obtained by the blood vessel data set, and determining whether the metastasis occurs, specifically, acquiring morphological characteristics of peripheral blood vessels and morphological characteristics of intersection parts, if the morphological characteristics are distorted or deformed, determining the position association relationship between the tumor and the blood vessels, and if the morphological characteristics are close to each other, indicating that the deformation or distortion of the blood vessels is caused by the tumor, and therefore, determining that the tumor has the metastasis.
It can be seen that, with the gastric tumor identification method based on VRDS 4D medical imaging provided in the embodiments of the present application, the medical imaging apparatus first obtains a stomach scanning image of the target object by VRDS 4D imaging technology, processing the stomach scanning image according to the target position of the tumor to obtain stomach 4D image data of a target object according to the target position of the tumor, extracting stomach wall 4D image data of the stomach wall from the target position stomach 4D image data of the tumor, determining the target position of the tumor according to the target position of the tumor and determining the stomach tumor type corresponding to the target object according to the target position of the tumor, thus, the position of the tumor can be accurately positioned based on the 4D image data, and the accuracy of judging the type of the gastric tumor is improved.
In accordance with the above, please refer to fig. 3, which is a schematic structural diagram of a medical imaging apparatus 300 provided in an embodiment of the present application, as shown in the figure, the medical imaging apparatus 300 includes a processor 310, a memory 320, a communication interface 330, and one or more programs 321, wherein the one or more programs 321 are stored in the memory 320 and configured to be executed by the processor 310, and the one or more programs 321 include instructions for:
acquiring a stomach scanning image of a target object by a VRDS 4D imaging technology;
processing the stomach scanning image to obtain stomach 4D image data of the target object;
extracting stomach wall 4D image data located on a stomach wall from the stomach 4D image data;
determining a target position of a tumor according to the 4D image data of the stomach wall;
and determining the stomach tumor type corresponding to the target object according to the target position of the tumor.
It can be seen that, with the medical imaging device provided in the embodiment of the present application, a stomach scan image of a target object can be obtained by a VRDS 4D imaging technology, the stomach scan image of a target position according to a tumor is processed to obtain stomach 4D image data of the target object of the target position according to the tumor, the stomach wall 4D image data located on the stomach wall is extracted from the stomach 4D image data of the target position according to the tumor, the target position of the tumor is determined according to the stomach wall 4D image data of the target position of the tumor, and a stomach tumor type corresponding to the target object is determined according to the target position of the tumor.
In one possible example, in determining a target location of a tumor from the 4D image data of the stomach wall, the program further comprises instructions for:
determining mucosa layer image data, submucosa layer image data, muscularis layer image data and stomach wall image data corresponding to the stomach wall of the target object according to the 4D stomach wall image data;
determining abnormal data corresponding to the stomach wall according to the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data;
and determining the target position corresponding to the tumor according to the abnormal data.
In one possible example, in determining, from the 4D image data of the stomach wall, corresponding mucosal layer image data, submucosal layer image data, muscularis layer image data, and extramural wall image data of the target subject, the program further includes instructions for:
according to the 4D image data of the stomach wall, determining the space coordinate of each data in the 4D image data of the stomach wall to obtain a plurality of space coordinates;
according to the plurality of space coordinates, determining a plurality of target space coordinates corresponding to the stomach wall corresponding to the target object, wherein the stomach wall comprises at least one of the following from inside to outside: mucosal layer, submucosa, muscularis layer and stomach wall;
determining a mucosa layer space coordinate set, a submucosa layer space coordinate set, a muscularis layer space coordinate set and a stomach outer wall space coordinate set according to a plurality of target space coordinates corresponding to the stomach wall;
and determining the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data corresponding to the target object according to the mucosa layer space coordinate set, the submucosa layer space coordinate set, the muscularis layer space coordinate set and the stomach outer wall space coordinate set.
In one possible example, in determining corresponding abnormality data for the stomach wall based on the mucosal layer image data, the submucosal layer image data, the muscularis layer image data, and the outer stomach wall image data, the program further includes instructions for:
according to the mucosa layer image data, the submucosal layer image data, the muscularis image data and the stomach outer wall image data, mucosa layer characteristic data, submucosal layer characteristic data, muscularis and stomach outer wall characteristic data which respectively correspond to the mucosa layer, the submucosal layer, the muscularis and the stomach outer wall are generated, and the characteristic data comprise at least one of the following data: spatial position, height, thickness, diameter and shape;
matching the mucosa characteristic data, the submucosal layer characteristic data and the stomach outer wall characteristic data with a preset normal data set corresponding to the stomach wall to obtain a plurality of matching values, wherein the preset stomach wall normal characteristic data set comprises at least one of the following data: mucosal layer standard characteristic data, submucosal layer standard characteristic data and stomach outer wall standard characteristic data;
and selecting characteristic data corresponding to the matching value smaller than or equal to a preset threshold value from the plurality of matching values as abnormal data corresponding to the stomach wall.
In one possible example, in determining the target location corresponding to the tumor from the anomaly data, the program further includes instructions for:
screening a plurality of spatial position data corresponding to the abnormal data;
traversing parts corresponding to the plurality of spatial position data to obtain a plurality of target spatial position data corresponding to the tumor;
and positioning the tumor according to the plurality of target space position data to obtain the target position corresponding to the tumor.
In one possible example, in traversing a region corresponding to the plurality of spatial location data to obtain a plurality of target spatial location data corresponding to the tumor, the program further includes instructions for:
determining a three-dimensional space region corresponding to the plurality of spatial position data according to the plurality of spatial position data;
dividing the three-dimensional space region into K layers of subspaces from top to bottom, wherein K is a positive integer greater than 1;
and traversing the K-layer subspace to obtain an L-layer target subspace corresponding to the tumor, wherein a plurality of spatial position data corresponding to the L-layer target subspace are the target spatial position data, and L is a positive integer less than or equal to K.
In one possible example, in determining a gastric tumor category corresponding to the target object based on the target location of the tumor, the program further includes instructions for:
determining the depth of the tumor infiltrated relative to the stomach wall and morphological characteristics of the tumor according to the abnormal data corresponding to the target position of the tumor;
determining the type of the tumor according to the target position of the tumor, the depth and the morphological characteristics.
In one possible example, if the stomach 4D image data includes a vessel data set, the program further includes instructions for:
classifying the blood vessel data sets to obtain peripheral blood vessel data sets and peripheral cross blood vessel data sets;
if the target position of the tumor infiltrates into the outer wall of the stomach, determining the position association relationship between the tumor and peripheral blood vessels according to a peripheral blood vessel data set and a peripheral cross blood vessel data set;
and if the position association relationship is close, determining that the tumor has metastasis.
In one possible example, in processing the stomach scan image to obtain stomach 4D imagery data of the target subject, the program further comprises instructions to:
performing first preset processing on the stomach scanning image 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 a stomach data set and a blood vessel data set, and the stomach data set comprises a gastric mucosa data set;
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 a blood vessel data set;
executing a second preset process on the second medical image data to obtain the target 4D image, wherein the 4D image data includes: stomach 4D image data and a blood vessel data set.
In accordance with the above, an apparatus for implementing the above-described gastric tumor identification method based on VRDS 4D medical images is as follows:
please refer to fig. 4, which is a schematic structural diagram of an embodiment of a gastric tumor identification apparatus based on VRDS 4D medical images according to the present application. The gastric tumor recognition device based on VRDS 4D medical image described in the embodiment comprises: the obtaining unit 401, the processing unit 402, the extracting unit 403, and the determining unit 404 are as follows:
the acquiring unit 401 is configured to acquire a stomach scanning image of a target object by using a VRDS 4D imaging technology;
the processing unit 402 is configured to process the stomach scan image to obtain stomach 4D image data of the target object;
the extracting unit 403 is configured to extract 4D image data of a stomach wall located on the stomach wall from the 4D image data of the stomach;
the determination unit 404 is configured to determine a target position of a tumor according to the 4D image data of the stomach wall;
the determining unit 404 is further configured to determine a gastric tumor type corresponding to the target object according to the target location of the tumor.
It can be seen that, with the gastric tumor recognition device based on VRDS 4D medical images described in the embodiment of the present application, a stomach scan image of a target object can be obtained through a VRDS 4D imaging technology, the stomach scan image according to the target position of the tumor is processed to obtain stomach 4D image data of the target object according to the target position of the tumor, the stomach wall 4D image data located on the stomach wall is extracted from the stomach 4D image data according to the target position of the tumor, the target position of the tumor is determined according to the stomach wall 4D image data of the target position of the tumor, and the type of the gastric tumor corresponding to the target object is determined according to the target position of the tumor, so that the position of the tumor can be accurately located based on the 4D image data, which is beneficial to improving the accuracy of determining the type of the gastric tumor.
In one possible example, in said determining a target location of a tumor from said 4D image data of the stomach wall, said determining unit 404 is specifically configured to:
determining mucosa layer image data, submucosa layer image data, muscularis layer image data and stomach wall image data corresponding to the stomach wall of the target object according to the 4D stomach wall image data;
determining abnormal data corresponding to the stomach wall according to the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data;
and determining the target position corresponding to the tumor according to the abnormal data.
In one possible example, in determining mucosa image data, submucosa image data, muscularis image data and extramural image data corresponding to the stomach wall of the target subject according to the stomach wall 4D image data, the determining unit 404 is further specifically configured to:
according to the 4D image data of the stomach wall, determining the space coordinate of each data in the 4D image data of the stomach wall to obtain a plurality of space coordinates;
according to the plurality of space coordinates, determining a plurality of target space coordinates corresponding to a stomach wall corresponding to the target object, wherein the stomach wall comprises at least one of the following from inside to outside: mucosal layer, submucosa, muscularis layer and stomach wall;
determining a mucosa layer space coordinate set, a submucosa layer space coordinate set, a muscularis layer space coordinate set and a stomach outer wall space coordinate set according to a plurality of target space coordinates corresponding to the stomach wall;
and determining the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data corresponding to the target object according to the mucosa layer space coordinate set, the submucosa layer space coordinate set, the muscularis layer space coordinate set and the stomach outer wall space coordinate set.
In one possible example, in terms of determining the abnormal data corresponding to the stomach wall according to the mucosal layer image data, the submucosal layer image data, the muscularis layer image data and the stomach outer wall image data, the determining unit 404 is specifically further configured to:
according to the mucosa layer image data, the submucosal layer image data, the muscularis image data and the stomach outer wall image data, mucosa layer characteristic data, submucosal layer characteristic data, muscularis and stomach outer wall characteristic data which respectively correspond to the mucosa layer, the submucosal layer, the muscularis and the stomach outer wall are generated, and the characteristic data comprise at least one of the following data: spatial position, height, thickness, diameter and shape;
matching the mucosa characteristic data, the submucosal layer characteristic data and the stomach outer wall characteristic data with a preset normal data set corresponding to the stomach wall to obtain a plurality of matching values, wherein the preset stomach wall normal characteristic data set comprises at least one of the following data: mucosal layer standard characteristic data, submucosal layer standard characteristic data and stomach outer wall standard characteristic data;
and selecting characteristic data corresponding to the matching value smaller than or equal to a preset threshold value from the plurality of matching values as abnormal data corresponding to the stomach wall.
In a possible example, when determining the target position aspect corresponding to the tumor according to the abnormal data, the determining unit 404 is further specifically configured to:
screening a plurality of spatial position data corresponding to the abnormal data;
traversing parts corresponding to the plurality of spatial position data to obtain a plurality of target spatial position data corresponding to the tumor;
and positioning the tumor according to the plurality of target space position data to obtain the target position corresponding to the tumor.
In a possible example, in terms of traversing the corresponding sites of the plurality of spatial position data to obtain a plurality of target spatial position data corresponding to the tumor, the determining unit 404 is further specifically configured to:
determining a three-dimensional space region corresponding to the plurality of spatial position data according to the plurality of spatial position data;
dividing the three-dimensional space region into K layers of subspaces from top to bottom, wherein K is a positive integer greater than 1;
and traversing the K-layer subspace to obtain an L-layer target subspace corresponding to the tumor, wherein a plurality of spatial position data corresponding to the L-layer target subspace are the target spatial position data, and L is a positive integer less than or equal to K.
In one possible example, in said determining the kind of the gastric tumor corresponding to the target object according to the target location of the tumor, the determining unit 404 is further configured to:
determining the depth of the tumor infiltrated relative to the stomach wall and morphological characteristics of the tumor according to the abnormal data corresponding to the target position of the tumor;
determining the type of the tumor according to the target position of the tumor, the depth and the morphological characteristics.
In one possible example, in the processing the stomach scan image to obtain the stomach 4D image data of the target subject, the processing unit 402 is specifically configured to:
performing first preset processing on the stomach scanning image 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 a stomach data set and a blood vessel data set, and the stomach data set comprises a gastric mucosa data set;
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 a blood vessel data set;
executing a second preset process on the second medical image data to obtain the target 4D image, wherein the 4D image data includes: stomach 4D image data and a blood vessel data set.
It is understood that the functions of the program modules of the gastric tumor identification apparatus based on VRDS 4D medical images according to the present embodiment can be implemented according to the method in the foregoing method embodiment, and the implementation process thereof can refer to the related description of the foregoing method embodiment, and will not be described herein again.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the VRDS 4D medical image-based gastric tumor identification methods as described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the VRDS 4D medical image-based gastric tumor identification methods as set forth in the above method embodiments.
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 will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in 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 embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be 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 some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (20)
- A gastric tumor identification method based on VRDS 4D medical images is characterized by being applied to a medical imaging device, and the method comprises the following steps:acquiring a stomach scanning image of a target object by a VRDS 4D imaging technology;processing the stomach scanning image to obtain stomach 4D image data of the target object;extracting stomach wall 4D image data located on a stomach wall from the stomach 4D image data;determining a target position of a tumor according to the 4D image data of the stomach wall;and determining the stomach tumor type corresponding to the target object according to the target position of the tumor.
- The method according to claim 1, wherein determining a target location of a tumor from the 4D image data of the stomach wall comprises:determining mucosa layer image data, submucosa layer image data, muscularis layer image data and stomach wall image data corresponding to the stomach wall of the target object according to the 4D stomach wall image data;determining abnormal data corresponding to the stomach wall according to the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data;and determining the target position corresponding to the tumor according to the abnormal data.
- The method according to claim 2, wherein determining mucosal layer image data, submucosal layer image data, muscularis layer image data and extramural wall image data corresponding to the target subject's stomach wall from the stomach wall 4D image data comprises:according to the 4D image data of the stomach wall, determining the space coordinate of each data in the 4D image data of the stomach wall to obtain a plurality of space coordinates;according to the plurality of space coordinates, determining a plurality of target space coordinates corresponding to the stomach wall corresponding to the target object, wherein the stomach wall comprises at least one of the following from inside to outside: mucosal layer, submucosa, muscularis layer and stomach wall;determining a mucosa layer space coordinate set, a submucosa layer space coordinate set, a muscularis layer space coordinate set and a stomach outer wall space coordinate set according to a plurality of target space coordinates corresponding to the stomach wall;and determining the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data corresponding to the target object according to the mucosa layer space coordinate set, the submucosa layer space coordinate set, the muscularis layer space coordinate set and the stomach outer wall space coordinate set.
- The method of claim 2, wherein determining the corresponding abnormality data for the stomach wall from the mucosal layer image data, the submucosal layer image data, the muscularis layer image data, and the outer stomach wall image data comprises:according to the mucosa layer image data, the submucosal layer image data, the muscularis image data and the stomach outer wall image data, mucosa layer characteristic data, submucosal layer characteristic data, muscularis and stomach outer wall characteristic data which respectively correspond to the mucosa layer, the submucosal layer, the muscularis and the stomach outer wall are generated, and the characteristic data comprise at least one of the following data: spatial position, height, thickness, diameter and shape;matching the mucosa characteristic data, the submucosal layer characteristic data and the stomach outer wall characteristic data with a preset normal data set corresponding to the stomach wall to obtain a plurality of matching values, wherein the preset stomach wall normal characteristic data set comprises at least one of the following data: mucosal layer standard characteristic data, submucosal layer standard characteristic data and stomach outer wall standard characteristic data;and selecting characteristic data corresponding to the matching value smaller than or equal to a preset threshold value from the plurality of matching values as abnormal data corresponding to the stomach wall.
- The method of claim 4, wherein determining the target location corresponding to the tumor based on the anomaly data comprises:screening a plurality of spatial position data corresponding to the abnormal data;traversing parts corresponding to the plurality of spatial position data to obtain a plurality of target spatial position data corresponding to the tumor;and positioning the tumor according to the plurality of target space position data to obtain the target position corresponding to the tumor.
- The method of claim 5, wherein traversing the region corresponding to the plurality of spatial location data to obtain a plurality of target spatial location data corresponding to the tumor comprises:determining a three-dimensional space region corresponding to the plurality of spatial position data according to the plurality of spatial position data;dividing the three-dimensional space region into K layers of subspaces from top to bottom, wherein K is a positive integer greater than 1;and traversing the K-layer subspace to obtain an L-layer target subspace corresponding to the tumor, wherein a plurality of spatial position data corresponding to the L-layer target subspace are the target spatial position data, and L is a positive integer less than or equal to K.
- The method of claim 2, wherein determining the target object's corresponding gastric tumor type based on the target location of the tumor comprises:determining the depth of the tumor infiltrated relative to the stomach wall and morphological characteristics of the tumor according to the abnormal data corresponding to the target position of the tumor;determining the type of the tumor according to the target position of the tumor, the depth and the morphological characteristics.
- The method of claim 7, wherein if the stomach 4D image data comprises a vascular data set, the method further comprises:classifying the blood vessel data sets to obtain peripheral blood vessel data sets and peripheral cross blood vessel data sets;if the target position of the tumor infiltrates into the outer wall of the stomach, determining the position association relationship between the tumor and peripheral blood vessels according to a peripheral blood vessel data set and a peripheral cross blood vessel data set;and if the position association relationship is close, determining that the tumor has metastasis.
- The method of claim 1, wherein said processing the stomach scan image to obtain stomach 4D image data of the target subject comprises:performing first preset processing on the stomach scanning image 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 a stomach data set and a blood vessel data set, and the stomach data set comprises a gastric mucosa data set;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 a blood vessel data set;executing a second preset process on the second medical image data to obtain the target 4D image, wherein the 4D image data includes: stomach 4D image data and a blood vessel data set.
- A gastric tumor identification device based on VRDS 4D medical images, which is applied to a medical imaging device, and comprises:the acquisition unit is used for acquiring a stomach scanning image of the target object by a VRDS 4D imaging technology;the processing unit is used for processing the stomach scanning image to obtain stomach 4D image data of the target object;an extraction unit for extracting stomach wall 4D image data located on a stomach wall from the stomach 4D image data;a determination unit for determining a target position of a tumor from the 4D image data of the stomach wall;the determining unit is further configured to determine a gastric tumor type corresponding to the target object according to the target position of the tumor.
- The apparatus according to claim 10, wherein in said determining a target location of a tumor from the 4D image data of the stomach wall, the determining unit is specifically configured to:determining mucosa layer image data, submucosa layer image data, muscularis layer image data and stomach wall image data corresponding to the stomach wall of the target object according to the 4D stomach wall image data;determining abnormal data corresponding to the stomach wall according to the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data;and determining the target position corresponding to the tumor according to the abnormal data.
- The apparatus according to claim 11, wherein in determining the mucosa image data, submucosa image data, muscularis image data and extramural image data corresponding to the stomach wall of the target subject from the stomach wall 4D image data, the determining unit is further configured to:according to the 4D image data of the stomach wall, determining the space coordinate of each data in the 4D image data of the stomach wall to obtain a plurality of space coordinates;according to the plurality of space coordinates, determining a plurality of target space coordinates corresponding to a stomach wall corresponding to the target object, wherein the stomach wall comprises at least one of the following from inside to outside: mucosal layer, submucosa, muscularis layer and stomach wall;determining a mucosa layer space coordinate set, a submucosa layer space coordinate set, a muscularis layer space coordinate set and a stomach outer wall space coordinate set according to a plurality of target space coordinates corresponding to the stomach wall;and determining the mucosa layer image data, the submucosa layer image data, the muscularis layer image data and the stomach outer wall image data corresponding to the target object according to the mucosa layer space coordinate set, the submucosa layer space coordinate set, the muscularis layer space coordinate set and the stomach outer wall space coordinate set.
- The device according to claim 11, wherein in determining the corresponding abnormality data of the stomach wall from the mucosal layer image data, the submucosal layer image data, the muscularis layer image data and the image data of the outer stomach wall, the determining unit is further configured to:according to the mucosa layer image data, the submucosal layer image data, the muscularis image data and the stomach outer wall image data, mucosa layer characteristic data, submucosal layer characteristic data, muscularis and stomach outer wall characteristic data which respectively correspond to the mucosa layer, the submucosal layer, the muscularis and the stomach outer wall are generated, and the characteristic data comprise at least one of the following data: spatial position, height, thickness, diameter and shape;matching the mucosa characteristic data, the submucosal layer characteristic data and the stomach outer wall characteristic data with a preset normal data set corresponding to the stomach wall to obtain a plurality of matching values, wherein the preset stomach wall normal characteristic data set comprises at least one of the following data: mucosal layer standard characteristic data, submucosal layer standard characteristic data and stomach outer wall standard characteristic data;and selecting characteristic data corresponding to the matching value smaller than or equal to a preset threshold value from the plurality of matching values as abnormal data corresponding to the stomach wall.
- The apparatus according to claim 13, wherein in said determining the target location corresponding to the tumor according to the anomaly data, the determining unit is further configured to:screening a plurality of spatial position data corresponding to the abnormal data;traversing parts corresponding to the plurality of spatial position data to obtain a plurality of target spatial position data corresponding to the tumor;and positioning the tumor according to the plurality of target space position data to obtain the target position corresponding to the tumor.
- The apparatus according to claim 14, wherein in the traversing of the corresponding portion of the plurality of spatial position data to obtain a plurality of target spatial position data corresponding to the tumor, the determining unit is further configured to:determining a three-dimensional space region corresponding to the plurality of spatial position data according to the plurality of spatial position data;dividing the three-dimensional space region into K layers of subspaces from top to bottom, wherein K is a positive integer greater than 1;and traversing the K-layer subspace to obtain an L-layer target subspace corresponding to the tumor, wherein a plurality of spatial position data corresponding to the L-layer target subspace are the target spatial position data, and L is a positive integer less than or equal to K.
- The apparatus according to claim 11, wherein in said determining the type of the gastric tumor corresponding to the target object based on the target location of the tumor, the determining unit is further configured to:determining the depth of the tumor infiltrated relative to the stomach wall and morphological characteristics of the tumor according to the abnormal data corresponding to the target position of the tumor;determining the type of the tumor according to the target position of the tumor, the depth and the morphological characteristics.
- The apparatus according to claim 16, wherein in said processing the stomach scan image to obtain stomach 4D image data of the target subject, the processing unit is specifically configured to:performing first preset processing on the stomach scanning image 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 a stomach data set and a blood vessel data set, and the stomach data set comprises a gastric mucosa data set;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 a blood vessel data set;executing a second preset process on the second medical image data to obtain the target 4D image, wherein the 4D image data includes: stomach 4D image data and a blood vessel data set.
- A medical imaging apparatus comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-9.
- A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-9.
- A computer program product, characterized in that the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform the method according to any one of claims 1-9.
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