CN114341996A - Disease analysis method based on VRDS 4D and related product - Google Patents
Disease analysis method based on VRDS 4D and related product Download PDFInfo
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Abstract
A VRDS 4D-based disease analysis method and related products are applied to a medical imaging device, and the method comprises the following steps: the method comprises the steps of obtaining scanning images of a plurality of human organs of a target user to obtain a plurality of scanning images, wherein the human organs comprise at least one of ear parts, nose parts and throat parts, processing the plurality of scanning images to obtain target 4D images corresponding to the target user, the target 4D images comprise target image data corresponding to the target user, identifying diseased parts corresponding to the target user based on the target image data, the diseased parts are one or more parts in the plurality of human organs, and analyzing the disease conditions of the diseased parts to obtain target analysis results.
Description
The application relates to the technical field of medical imaging devices, in particular to a VRDS 4D-based disease analysis method 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 correlation between the tumor and the peripheral blood vessels cannot be determined by only directly viewing the two-dimensional slice data, which seriously affects the diagnosis of the disease by the doctor.
Disclosure of Invention
The embodiment of the application provides a disease analysis method based on VRDS 4D and a related product, and is beneficial to improving the efficiency of disease analysis.
In a first aspect, the present application provides a VRDS 4D-based disease analysis method, including:
obtaining scanning images of a plurality of human organs of a target user to obtain a plurality of scanning images, wherein each human organ at least corresponds to one scanning image, and the human organs comprise at least one of the following parts: ear, nose and throat;
processing the plurality of scanning images to obtain a target 4D image corresponding to the target user, wherein the target 4D image comprises target image data corresponding to the target user;
identifying a diseased part corresponding to the target user based on the target image data, wherein the diseased part is one or more parts in the plurality of human organs;
and analyzing the disease condition of the diseased part to obtain a target disease condition analysis result.
In a second aspect, an embodiment of the present application provides a VRDS 4D-based disease analysis device, including:
the device comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring scanning images of a plurality of human organs of a target user to obtain a plurality of scanning images, and each human organ at least corresponds to one scanning image;
the processing unit is used for processing the plurality of scanning images to obtain a target 4D image corresponding to the target user, wherein the target 4D image comprises target image data corresponding to the target user;
the identification unit is used for identifying a disease part corresponding to the target user based on the target image data, wherein the disease part is one or more parts in the plurality of human organs;
and the analysis unit is used for analyzing the disease condition of the diseased part to obtain a target disease condition analysis result. 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 flowchart illustrating an embodiment of a VRDS 4D-based disease analysis method according to an embodiment of the present disclosure;
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 condition analysis device based on VRDS 4D 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 medical condition analyzing and processing system 100 based on VRDS 4D provided in the embodiment of the present application is shown, the system 100 includes a medical imaging apparatus 110 and a network database 120, wherein the medical imaging apparatus 110 may include a local medical imaging apparatus 111 and/or a terminal medical imaging apparatus 112, the local medical imaging apparatus 111 or the terminal medical imaging apparatus 112 is used for performing identification, positioning, four-dimensional volume rendering, and anomaly analysis of a human organ based on original DICOM data based on a medical condition analyzing algorithm of VRDS 4D presented in the embodiment of the present application, so as to achieve a four-dimensional stereoscopic imaging effect (the 4-dimensional medical image specifically means that the medical image includes an internal spatial structural feature and an external spatial structural feature of a displayed tissue, the internal spatial structural feature means that slice data inside the tissue is not lost, that is, the medical imaging apparatus may present the internal structure of the tissues such as the human organ and the blood vessel, and the external spatial structural characteristics refer to the environmental characteristics between the tissues, including the spatial position characteristics (including intersection, spacing, fusion) between the tissues, and the like, the edge structural characteristics of the intersection position between the organs such as the ear, the nose, and the throat, and the blood vessel, and the like), the local medical imaging apparatus 111 may also be used for editing the image source data with respect to the terminal medical imaging apparatus 112, forming 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 the human organ and in the human organ, and the transfer function result of the cubic space, such as the number of arrays, coordinates, colors, transparencies of the cubic editing frame and the arc editing required by the transfer function. 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 through 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 an external shooting device of the medical imaging apparatus, such as a mouse, a keyboard, a tablet computer (Pad), an ipad (internet enabled 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 'internal observation of a human body organ, real-time shearing effect rendering', and (5) moving the view up and down.
The following describes in detail a method for analyzing a disease condition based on VRDS 4D according to the present embodiment.
Please refer to fig. 2, which is a flowchart illustrating an embodiment of a VRDS 4D-based disease analysis method according to the present application. The VRDS 4D-based disease analysis method described in this example includes the following steps:
201. the medical imaging device obtains scanning images of a plurality of human organs of a target user to obtain a plurality of scanning images, each human organ at least corresponds to one scanning image, and the human organs comprise at least one of the following parts: ear, nose and throat;
wherein, the human organ can be at least one of the following: organs such as ear, nose, throat, brain, kidney, etc., but not limited thereto, the scan image may include any one of the following: CT images, MRI images, DTI images, PET-CT images, etc., without limitation. The medical imaging device can acquire a plurality of scanning images reflecting the internal structures of a plurality of human organs of a target user, wherein each human organ at least corresponds to one scanning image.
202. The medical imaging device processes the multiple scanning images to obtain a target 4D image corresponding to the target user, wherein the target 4D image comprises target image data corresponding to the target user;
the target 4D image corresponding to the target user can be obtained by processing a plurality of scanned images, and the target image data can include a human organ data set and a blood vessel data set, and the human organ data set can include data corresponding to a plurality of human organs.
Optionally, in the step 202, the processing the multiple scanned images to obtain a target 4D image corresponding to the target user, where the target 4D image includes target image data corresponding to the target user, may include the following steps:
21. executing first preset processing on the multiple scanning images 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 human organ data set and a blood vessel data set, and the human organ data set comprises a plurality of data corresponding to a plurality of human organs;
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 the human organ data set and the blood vessel data set;
24. and executing second preset processing aiming at the second medical image data to obtain the target 4D image.
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; the VRDS medical network model can be preset in the medical imaging device, the medical imaging device obtains a BMP data source by processing a plurality of pieces of scanned 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.
Furthermore, the medical imaging apparatus imports the BMP data source into a preset VRDS medical network model, can call each transfer function in a pre-stored transfer function set through the VRDS medical network model, and processes the BMP data source through a plurality of transfer functions in the transfer function set to obtain the first medical image data, where the transfer function set may include a transfer function of a blood vessel and a transfer function of the plurality of human organs, which are preset by a reverse editor, and where the transfer functions of the plurality of human organs may include at least one of: the transfer function of ear, the transfer function of nose and the transfer function of larynx, like this, obtain first medical image data through predetermineeing VRDS medical network model, can improve the accuracy and the efficiency of obtaining data.
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, an ear data set, a nose data set, a throat data set and a blood vessel data set are obtained, the blood vessel data set comprises data related to the cross positions of the organs and the blood vessels, and finally, second medical image data can be obtained.
In one possible example, the vessel centerline data of the vessel may be determined from the vessel data set, the vessel centerline may refer to a connection line between each central point in the vessel profile, and the vessel centerline extraction method may include at least one of: the method includes the steps of manually calibrating, transforming distances, refining topology and the like, and is not limited herein, specifically, determining a plurality of forming directions corresponding to a plurality of blood vessels through data of blood vessel intersecting portions in the blood vessel data set, and determining a position and a direction of a blood vessel centerline of each of the plurality of blood vessels in the blood vessel according to the plurality of forming directions, so that a blood vessel centerline data set corresponding to each blood vessel can be obtained according to the extraction method, the blood vessel centerline data set includes blood vessel centerlines of the plurality of blood vessels, and the blood vessel centerlines can be prepared for subsequently determining a disease part.
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 contextual semantic information of the segmented target in the whole image, namely, features reflecting the relation between the segmented target and the environment, the features are used for judging object types, and the high-resolution information is used for providing more refined features such as gradients and the like for the segmented target, wherein the segmented target can comprise ear, nose and throat and blood vessels, so that the second medical image data can be processed to obtain a target 4D image, the target 4D image can comprise target image data, and the target image data can comprise at least one of the following: a blood vessel data set, an ear data set, a nose data set, and a throat data set.
203. The medical imaging device identifies a disease part corresponding to the target user based on the target image data, wherein the disease part is one or more parts in the plurality of human organs;
wherein, the disease part can comprise at least one of the following parts: the diseased region may be a specific diseased region of a plurality of human organs, and may be one or more regions of a plurality of human organs, and the disease type corresponding to the diseased region may be a tumor sac or polyp, and the like, but is not limited thereto.
Optionally, in step 203, if the target image data at least includes a blood vessel data set, identifying a diseased part corresponding to the target user based on the target image data may include the following steps:
31. selecting a plurality of target vessel data sets associated with the plurality of human organs based on the vessel data sets;
32. obtaining a blood vessel central line data set corresponding to the plurality of target blood vessel data sets, wherein the blood vessel central line is a connecting line between each central point in the target blood vessel section;
33. determining a vascularity data set corresponding to the plurality of human organs according to the blood vessel centerline data set, wherein each of the vascularity data sets corresponds to one of the human organs;
34. determining a plurality of target organs from the vascularity data set, the plurality of target organs including at least one of: ear, nose and throat;
35. acquiring a plurality of image data corresponding to the target organs in the target image set, wherein each target organ corresponds to one image data;
36. and identifying the corresponding diseased part of the target user according to the plurality of image data.
Wherein, the target image data at least includes a blood vessel data set, because the scanned image obtained by scanning includes images of a plurality of blood vessels, a plurality of target blood vessel data sets associated with a plurality of human organs can be selected based on the blood vessel data sets, a plurality of target blood vessels corresponding to the plurality of target blood vessel data sets are all connected with the plurality of human organs, a blood vessel center line data set corresponding to the plurality of target blood vessels can be obtained through the plurality of target blood vessel data sets, the blood vessel center line data set includes a plurality of blood vessel center lines corresponding to the plurality of target blood vessels, because each target blood vessel has a width, if the target blood vessel has a lesion, the contour becomes different, which is not beneficial to observing information such as bifurcation, position of the target blood vessel, and the like, therefore, the blood vessel center line can be introduced, and then, the specific position of the target blood vessel can be obtained through the position of the blood vessel center line, in this way, a distribution corresponding to a plurality of target blood vessels connected to a plurality of human organs can be obtained, and a vascularity data set including the positions of the blood vessels, the shape characteristics of the blood vessels, and the like can be obtained.
In addition, a plurality of target organs connected with blood vessels can be obtained according to the blood vessel distribution data set, the target organs can comprise ears, noses, throats and the like, and the target organs can be diseased target organs, so that specific diseased parts in the target organs can be determined, and subsequent disease analysis is facilitated.
Optionally, in the step 36, the identifying the diseased part corresponding to the target user according to the plurality of image data may include:
361. generating a plurality of feature information corresponding to the target organs according to the image data, wherein the feature information comprises at least one of the following: color, shape, location, size;
362. and identifying the disease part corresponding to the target user according to the characteristic information.
In order to identify the diseased part, a plurality of pieces of feature information corresponding to a plurality of target organs may be generated for a plurality of pieces of image data corresponding to the plurality of target organs, and the diseased part may be identified based on the feature information, and the feature information may include at least one of: color, shape, location, size, etc., and are not limited thereto.
In one possible example, the step 34 of determining a plurality of target organs from the vascularity data set may comprise the steps of:
a1, obtaining a blood vessel radius data set corresponding to the plurality of human organs in the blood vessel data set;
a2, determining a blood vessel distortion distribution data set corresponding to the blood vessel radius exceeding a preset threshold value in the blood vessel distribution data set according to the blood vessel radius data set;
a3, determining the blood vessel distortion range through the blood vessel distortion distribution data set;
a4, determining the plurality of target organs according to the blood vessel distortion distribution data set and the blood vessel distortion range.
The blood vessel data set may include data related to intersection positions of blood vessels and blood vessels, a plurality of blood vessels connected to a plurality of human organs may be determined through the blood vessel data set, and images corresponding to the plurality of blood vessels, respectively, may be acquired to determine radii of the plurality of blood vessels, and a blood vessel radius data set may be obtained, the blood vessel radius data set including radii of the plurality of blood vessels corresponding to the plurality of human organs.
In addition, the preset threshold may be set by the user or default by the system, the preset threshold may be understood as a radius corresponding to a normal blood vessel, if a lesion such as a hemangioma is generated in the blood vessel, and the radius of the blood vessel may be larger than the radius of the normal blood vessel, the blood vessel may be classified as a distorted blood vessel, or, in combination with the contour shape of the blood vessel, whether the blood vessel is a distorted blood vessel may be determined, the radius of the blood vessel exceeding the preset threshold may be selected from the blood vessel radius data set, a plurality of distorted blood vessels having a blood vessel radius exceeding the preset threshold may be determined, a distorted blood vessel distribution data set in the blood vessel distribution data set corresponding to a plurality of distorted blood vessels may be obtained, the data set includes distribution data corresponding to each distorted blood vessel, the distribution data includes blood vessel bifurcation information, location information, and the like, and the distribution information corresponding to the plurality of distorted blood vessels may be obtained, the multiple distorted positions where the blood vessels are distorted can be determined according to the blood vessel distortion distribution data set corresponding to the multiple distorted blood vessels, and the multiple target organs where the pathological changes occur can be determined according to the multiple distorted positions and the distribution information of the multiple distorted blood vessels.
Alternatively, in the step 37, identifying the diseased part in the target organs according to the feature information may include:
b1, acquiring a preset diseased part identification model;
b2, inputting the plurality of characteristic information into the preset diseased part identification model to obtain a characteristic probability corresponding to each characteristic information in the plurality of characteristic information to obtain a plurality of characteristic probabilities;
b3, calculating at least one piece of feature information corresponding to the probability value exceeding a preset probability value;
b4, determining the disease sites in the target organs according to the at least one characteristic information.
The preset diseased part identification model can be set by a user or defaulted by a system, the preset diseased part identification model can be a convolutional neural network, the preset probability value can be set by the user or defaulted by the system, and the characteristic information can include at least one of the following information: morphological features, color features, positional features, size features, and the like, without limitation; specifically, at least one piece of feature information corresponding to a value exceeding a preset probability value in the plurality of feature probabilities can be determined through a plurality of feature probabilities corresponding to the plurality of features, and a part corresponding to the at least one piece of feature information is a diseased part in the target organs with a plurality of diseases, so that the identification efficiency can be improved.
In a possible example, the step 203 of identifying the diseased part corresponding to the target user based on the target image data may further include the following steps:
c1, generating first target image data corresponding to any human organ i in the plurality of human organs according to the target image data, where the first target image data includes at least k spatial position data, the k spatial position data corresponds to k first image data, and each spatial position data corresponds to one first image data, where k is a positive integer;
and C2, identifying the corresponding diseased part of the target user according to the k spatial position data.
The medical imaging device can determine a spatial position area corresponding to each human organ data in a plurality of human organs according to a human organ data set in the target image data, and determine a position coordinate corresponding to each human organ data according to the spatial position area corresponding to each human organ data, so that k spatial position data corresponding to any human organ i can be obtained, and each spatial position data can correspond to one first image data in the target image data.
Optionally, in the step C2, the identifying the onset part corresponding to the target user according to the k spatial position data may include:
c21, positioning the human organ i according to the k space position data;
and C22, traversing the k first image data corresponding to the k spatial position data to obtain p abnormal first image data, wherein the parts corresponding to the p spatial position data corresponding to the p abnormal first image data are the affected parts, and p is an integer smaller than k.
The abnormal parts are the disease parts, wherein k position coordinates corresponding to the k spatial position data are traversed to obtain P abnormal first image data, and the abnormal parts are obtained from the abnormal first image data and are the disease parts, wherein k is a positive integer, and P is an integer smaller than k.
204. And the medical imaging device analyzes the disease condition of the diseased part to obtain a target disease condition analysis result.
After the disease part is identified, the disease part can be analyzed to obtain a target disease analysis result, so that the medicine can be taken according to the obtained disease analysis result, or the disease analysis result can be used as an auxiliary treatment scheme to help doctors to confirm the diagnosis.
Optionally, in step 204, analyzing the disease condition of the disease-affected part to obtain a target disease condition analysis result, the method may include the following steps:
41. acquiring a plurality of groups of preset disease characteristic information corresponding to a preset disease characteristic database, wherein the preset disease characteristic database comprises a plurality of disease types, and each disease type corresponds to one group of preset disease characteristic information;
42. matching a plurality of characteristic information corresponding to the disease part with the plurality of groups of preset disease characteristic information one by one to obtain a plurality of matching values, wherein each matching value corresponds to one disease type;
43. acquiring a disease type corresponding to the maximum matching value in the plurality of matching values as a target disease type;
44. and determining a target disease analysis result corresponding to the target user according to a preset mapping relation between the disease type and the disease analysis result.
Wherein, a disease characteristic database can be preset in the medical imaging device, a plurality of disease types can be preset in the preset disease database, each disease type can correspond to a group of preset disease characteristic information, and the disease types can include at least one of the following: ear tumors, nose tumors, throat tumors, and the like, without limitation; specifically, a plurality of characteristics corresponding to the diseased part may be placed in the preset disease characteristic database for matching to obtain a plurality of matching values, the plurality of matching values are a plurality of proportional values obtained by matching with a plurality of preset disease characteristic information corresponding to each disease in the preset disease characteristic database, and the disease type corresponding to the maximum matching value among the plurality of matching values is selected as the target disease type; the medical imaging device can also preset a mapping relation between the disease types and the disease analysis results, can determine target disease analysis results corresponding to the target disease types according to the mapping relation, can generate disease analysis data aiming at a plurality of preset disease characteristic information corresponding to each disease type, can obtain a plurality of disease analysis data aiming at a plurality of disease types, can preset disease analysis results aiming at each disease analysis data, and can correspond to different disease degrees, so that the target disease analysis results corresponding to the target disease types can be obtained, the disease analysis efficiency can be improved, and the disease processing efficiency can be improved.
It can be seen that, according to the method for analyzing an illness state based on VRDS 4D provided in the embodiment of the present application, the medical imaging device may first obtain the scanned images of a plurality of human organs of the target user, to obtain a plurality of scanned images, where each human organ corresponds to at least one scanned image, where the human organs include at least one of the following: ear, nose and throat, handle many scanning image, obtain the target 4D image that the target user corresponds, this target 4D image includes the target image data that the target user corresponds, based on target image data, the morbidity position that the discernment target user corresponds, the morbidity position is one or more positions in a plurality of human organs, carry out state of an illness analysis to the morbidity position, obtain target state of an illness analysis result, so, the accessible is to scanning image's analysis and processing, obtain the morbidity position of target user, and carry out state of an illness analysis to this morbidity position, in order to obtain state of an illness analysis result, be favorable to improving state of an illness accuracy and efficiency of analysis.
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:
obtaining scanning images of a plurality of human organs of a target user to obtain a plurality of scanning images, wherein each human organ at least corresponds to one scanning image, and the human organs comprise at least one of the following parts: ear, nose and throat;
processing the plurality of scanning images to obtain a target 4D image corresponding to the target user, wherein the target 4D image comprises target image data corresponding to the target user;
identifying a diseased part corresponding to the target user based on the target image data, wherein the diseased part is one or more parts in the plurality of human organs;
and analyzing the disease condition of the diseased part to obtain a target disease condition analysis result.
It can be seen that, with the medical imaging apparatus provided in the embodiment of the present application, a plurality of scanned images of a plurality of human organs of a target user may be obtained, so as to obtain a plurality of scanned images, where each human organ corresponds to at least one scanned image, where the human organs include at least one of the following: ear, nose and throat, handle many scanning image, obtain the target 4D image that the target user corresponds, this target 4D image includes the target image data that the target user corresponds, based on target image data, the morbidity position that the discernment target user corresponds, the morbidity position is one or more positions in a plurality of human organs, carry out state of an illness analysis to the morbidity position, obtain target state of an illness analysis result, so, the accessible is to scanning image's analysis and processing, obtain the morbidity position of target user, and carry out state of an illness analysis to this morbidity position, in order to obtain state of an illness analysis result, be favorable to improving state of an illness accuracy and efficiency of analysis.
In one possible example, if the target image data includes at least a blood vessel data set, the program further includes instructions for, in the identifying the target user's corresponding diseased part based on the target image data:
selecting a plurality of target vessel data sets associated with the plurality of human organs based on the vessel data sets;
obtaining a blood vessel central line data set corresponding to the plurality of target blood vessel data sets, wherein the blood vessel central line is a connecting line between each central point in the target blood vessel section;
determining a vascularity data set corresponding to the plurality of human organs according to the blood vessel centerline data set, wherein each of the vascularity data sets corresponds to one of the human organs;
determining a plurality of target organs from the vascularity data set, the plurality of target organs including at least one of: ear, nose and throat;
acquiring a plurality of image data corresponding to the target organs in the target image set, wherein each target organ corresponds to one image data;
and identifying the corresponding diseased part of the target user according to the plurality of image data.
In one possible example, in identifying a corresponding diseased part of the target user based on the plurality of image data, the program further includes instructions for:
generating a plurality of feature information corresponding to the target organs according to the image data, wherein the feature information comprises at least one of the following: color, shape, location, size;
and identifying the disease part corresponding to the target user according to the characteristic information.
In one possible example, in determining a plurality of target organs from the vascularity data set, the program further comprises instructions for:
obtaining a blood vessel radius data set corresponding to the plurality of human organs in the blood vessel data set;
determining a blood vessel distortion distribution data set corresponding to the blood vessel radius exceeding a preset threshold in the blood vessel distribution data set according to the blood vessel radius data set;
determining the vessel distortion range from the vessel distortion distribution data set;
determining the plurality of target organs from the vessel aberration distribution dataset and the vessel aberration range.
In one possible example, in identifying a site of morbidity in the plurality of target organs from the plurality of characteristic information, the program further comprises instructions for:
acquiring a preset diseased part identification model;
inputting the plurality of characteristic information into the preset diseased part identification model to obtain a characteristic probability corresponding to each characteristic information in the plurality of characteristic information to obtain a plurality of characteristic probabilities;
calculating at least one piece of feature information corresponding to the probability value exceeding a preset probability value;
determining a diseased site in the plurality of target organs according to the at least one characteristic information.
In one possible example, in performing a condition analysis for the disease site to obtain a target condition analysis result, the program further includes instructions for:
acquiring a plurality of groups of preset disease characteristic information corresponding to a preset disease characteristic database, wherein the preset disease characteristic database comprises a plurality of disease types, and each disease type corresponds to one group of preset disease characteristic information;
matching a plurality of characteristic information corresponding to the disease part with the plurality of groups of preset disease characteristic information one by one to obtain a plurality of matching values, wherein each matching value corresponds to one disease type;
acquiring a disease type corresponding to the maximum matching value in the plurality of matching values as a target disease type;
and determining a target disease analysis result corresponding to the target user according to a preset mapping relation between the disease type and the disease analysis result.
In one possible example, in identifying a corresponding diseased part of the target user based on the target imagery data, the program further includes instructions to:
generating first target image data corresponding to any human organ i in the plurality of human organs according to the target image data, wherein the first target image data comprises at least k spatial position data, the k spatial position data corresponds to k first image data, each spatial position data corresponds to one first image data, and k is a positive integer;
and identifying the diseased part corresponding to the target user according to the k spatial position data.
In one possible example, in the identifying the target user's corresponding diseased part from the k spatial position data, the program further includes instructions for:
positioning the human organ i according to the k spatial position data;
traversing the k first image data corresponding to the k spatial position data to obtain p abnormal first image data, wherein the parts corresponding to the p spatial position data corresponding to the p abnormal first image data are the disease parts, and p is an integer smaller than k.
In one possible example, in processing the multiple scan images to obtain a target 4D video corresponding to the target user, where the target 4D video includes target video data corresponding to the target user, the program further includes instructions for:
executing first preset processing on the multiple scanning images 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 human organ data set and a blood vessel data set, and the human organ data set comprises a plurality of human organ data corresponding to a plurality of human organs;
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 the human organ data set and the blood vessel data set;
and executing second preset processing aiming at the second medical image data to obtain the target 4D image.
In accordance with the above, the following is a device for carrying out the above-described VRDS 4D-based method for analyzing a disease state, specifically as follows:
please refer to fig. 4, which is a schematic structural diagram of an embodiment of a disease analysis device based on VRDS 4D according to an embodiment of the present application. The VRDS 4D-based disease analysis device described in this embodiment includes: the acquiring unit 401, the processing unit 402, the identifying unit 403 and the analyzing unit 404 are specifically as follows:
the acquiring unit 401 is configured to acquire scanned images of multiple human organs of a target user to obtain multiple scanned images, where each human organ corresponds to at least one scanned image;
the processing unit 402 is configured to process the multiple scanned images to obtain a target 4D image corresponding to the target user, where the target 4D image includes target image data corresponding to the target user;
the identifying unit 403 is configured to identify a diseased part corresponding to the target user based on the target image data, where the diseased part is one or more parts in the plurality of human organs;
the analysis unit 404 is configured to perform disease analysis on the diseased part to obtain a target disease analysis result.
It can be seen that, with the disease analysis apparatus based on VRDS 4D described in the embodiment of the present application, the scan images of a plurality of human organs of the target user can be obtained, so as to obtain a plurality of scan images, where each human organ corresponds to at least one scan image, where the human organs include at least one of the following: ear, nose and throat, handle many scanning image, obtain the target 4D image that the target user corresponds, this target 4D image includes the target image data that the target user corresponds, based on target image data, the morbidity position that the identification target user corresponds, the morbidity position is one or more positions in a plurality of human organs, carry out state of an illness analysis to the morbidity position, obtain target state of an illness analysis result, so, the accessible is to scanning image's analysis and processing, obtain the morbidity position of target user, and carry out state of an illness analysis to this morbidity position, in order to obtain state of an illness analysis result, be favorable to improving state of an illness's accuracy and efficiency.
In one possible example, in terms of identifying the diseased part corresponding to the target user based on the target image data, the identifying unit 403 is specifically configured to:
selecting a plurality of target vessel data sets associated with the plurality of human organs based on the vessel data sets;
obtaining a blood vessel central line data set corresponding to the plurality of target blood vessel data sets, wherein the blood vessel central line is a connecting line between each central point in the target blood vessel section;
determining a vascularity data set corresponding to the plurality of human organs according to the blood vessel centerline data set, wherein each of the vascularity data sets corresponds to one of the human organs;
determining a plurality of target organs from the vascularity data set, the plurality of target organs including at least one of: ear, nose and throat;
acquiring a plurality of image data corresponding to the target organs in the target image set, wherein each target organ corresponds to one image data;
and identifying the corresponding diseased part of the target user according to the plurality of image data.
In one possible example, in terms of identifying the diseased part corresponding to the target user according to the plurality of image data, the identifying unit 403 is further specifically configured to:
generating a plurality of feature information corresponding to the target organs according to the image data, wherein the feature information comprises at least one of the following: color, shape, location, size;
and identifying the disease part corresponding to the target user according to the characteristic information.
In one possible example, in determining a plurality of target organs from the vascularity data set, the above-mentioned identification unit 403 is further specifically configured to:
obtaining a blood vessel radius data set corresponding to the plurality of human organs in the blood vessel data set;
determining a blood vessel distortion distribution data set corresponding to the blood vessel radius exceeding a preset threshold in the blood vessel distribution data set according to the blood vessel radius data set;
determining the vessel distortion range from the vessel distortion distribution data set;
determining the plurality of target organs from the vessel aberration distribution dataset and the vessel aberration range.
In one possible example, in identifying the diseased part in the target organs according to the feature information, the identifying unit 403 is further configured to:
acquiring a preset diseased part identification model;
inputting the plurality of characteristic information into the preset diseased part identification model to obtain a characteristic probability corresponding to each characteristic information in the plurality of characteristic information to obtain a plurality of characteristic probabilities;
calculating at least one piece of feature information corresponding to the probability value exceeding a preset probability value;
determining a diseased site in the plurality of target organs according to the at least one characteristic information.
In one possible example, in terms of performing a disease analysis on the disease site to obtain a target disease analysis result, the analysis unit 404 is specifically configured to:
acquiring a plurality of groups of preset disease characteristic information corresponding to a preset disease characteristic database, wherein the preset disease characteristic database comprises a plurality of disease types, and each disease type corresponds to one group of preset disease characteristic information;
matching a plurality of characteristic information corresponding to the disease part with the plurality of groups of preset disease characteristic information one by one to obtain a plurality of matching values, wherein each matching value corresponds to one disease type;
acquiring a disease type corresponding to the maximum matching value in the plurality of matching values as a target disease type;
and determining a target disease analysis result corresponding to the target user according to a preset mapping relation between the disease type and the disease analysis result.
In one possible example, in terms of identifying the diseased part corresponding to the target user based on the target image data, the identifying unit 403 is further specifically configured to:
generating first target image data corresponding to any human organ i in the plurality of human organs according to the target image data, wherein the first target image data comprises at least k spatial position data, the k spatial position data corresponds to k first image data, and each spatial position data corresponds to one first image data, and k is a positive integer;
and identifying the diseased part corresponding to the target user according to the k spatial position data.
In one possible example, in terms of identifying the corresponding diseased part of the target user according to the k spatial position data, the identifying unit 403 is further specifically configured to:
positioning the human organ i according to the k spatial position data;
traversing the k first image data corresponding to the k spatial position data to obtain p abnormal first image data, wherein the parts corresponding to the p spatial position data corresponding to the p abnormal first image data are the disease parts, and p is an integer smaller than k.
In a possible example, in processing the multiple scan images to obtain a target 4D video corresponding to the target user, where the target 4D video includes target video data corresponding to the target user, the processing unit 402 is specifically configured to:
executing first preset processing on the multiple scanning images 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 human organ data set and a blood vessel data set, and the human organ data set comprises a plurality of human organ data corresponding to a plurality of human organs;
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 the human organ data set and the blood vessel data set;
and executing second preset processing aiming at the second medical image data to obtain the target 4D image.
It is understood that the functions of the program modules of the disease analysis device based on VRDS 4D of the present embodiment can be specifically implemented according to the method in the foregoing method embodiments, and the specific implementation process thereof can refer to the description related to the foregoing method embodiments, which is not 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-based disease analysis 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 of the VRDS 4D based disease analysis 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 method for analyzing a disease condition based on VRDS 4D, comprising:obtaining scanning images of a plurality of human organs of a target user to obtain a plurality of scanning images, wherein each human organ at least corresponds to one scanning image, and the human organs comprise at least one of the following parts: ear, nose and throat;processing the plurality of scanning images to obtain a target 4D image corresponding to the target user, wherein the target 4D image comprises target image data corresponding to the target user;identifying a diseased part corresponding to the target user based on the target image data, wherein the diseased part is one or more parts in the plurality of human organs;and analyzing the disease condition of the diseased part to obtain a target disease condition analysis result.
- The method of claim 1, wherein identifying the target user's corresponding diseased region based on the target image data if the target image data includes at least a blood vessel data set comprises:selecting a plurality of target vessel data sets associated with the plurality of human organs based on the vessel data sets;obtaining a blood vessel central line data set corresponding to the plurality of target blood vessel data sets, wherein the blood vessel central line is a connecting line between each central point in the target blood vessel section;determining a vascularity data set corresponding to the plurality of human organs according to the blood vessel centerline data set, wherein each of the vascularity data sets corresponds to one of the human organs;determining a plurality of target organs from the vascularity data set, the plurality of target organs including at least one of: ear, nose and throat;acquiring a plurality of image data corresponding to the target organs in the target image set, wherein each target organ corresponds to one image data;and identifying the corresponding diseased part of the target user according to the plurality of image data.
- The method of claim 2, wherein identifying the target user's corresponding diseased region based on the plurality of image data comprises:generating a plurality of feature information corresponding to the target organs according to the image data, wherein the feature information comprises at least one of the following: color, shape, location, size;and identifying the disease part corresponding to the target user according to the characteristic information.
- The method of claim 2, wherein said determining a plurality of target organs from said vascularity data set comprises:obtaining a blood vessel radius data set corresponding to the plurality of human organs in the blood vessel data set;determining a blood vessel distortion distribution data set corresponding to the blood vessel radius exceeding a preset threshold in the blood vessel distribution data set according to the blood vessel radius data set;determining the vessel distortion range from the vessel distortion distribution data set;determining the plurality of target organs from the vessel aberration distribution dataset and the vessel aberration range.
- The method of claim 3, wherein said identifying a site of morbidity in said plurality of target organs based on said plurality of characteristic information comprises:acquiring a preset diseased part identification model;inputting the plurality of characteristic information into the preset diseased part identification model to obtain a characteristic probability corresponding to each characteristic information in the plurality of characteristic information to obtain a plurality of characteristic probabilities;calculating at least one piece of feature information corresponding to the probability value exceeding a preset probability value;determining a diseased site in the plurality of target organs according to the at least one characteristic information.
- The method according to any one of claims 1 to 5, wherein said analyzing the disease condition at said disease site to obtain a target disease condition analysis result comprises:acquiring a plurality of groups of preset disease characteristic information corresponding to a preset disease characteristic database, wherein the preset disease characteristic database comprises a plurality of disease types, and each disease type corresponds to one group of preset disease characteristic information;matching a plurality of characteristic information corresponding to the disease part with the plurality of groups of preset disease characteristic information one by one to obtain a plurality of matching values, wherein each matching value corresponds to one disease type;acquiring a disease type corresponding to the maximum matching value in the plurality of matching values as a target disease type;and determining a target disease analysis result corresponding to the target user according to a preset mapping relation between the disease type and the disease analysis result.
- The method of claim 1, wherein the identifying of the target user's corresponding diseased portion based on the target image data further comprises:generating first target image data corresponding to any human organ i in the plurality of human organs according to the target image data, wherein the first target image data comprises at least k spatial position data, the k spatial position data corresponds to k first image data, and each spatial position data corresponds to one first image data, and k is a positive integer;and identifying the diseased part corresponding to the target user according to the k spatial position data.
- The method according to claim 7, wherein the identifying the target user's corresponding diseased part according to the k spatial position data comprises:positioning the human organ i according to the k spatial position data;traversing the k first image data corresponding to the k spatial position data to obtain p abnormal first image data, wherein the parts corresponding to the p spatial position data corresponding to the p abnormal first image data are the disease parts, and p is an integer smaller than k.
- The method according to claim 1, wherein the processing the plurality of scan images to obtain a target 4D video corresponding to the target user, the target 4D video including target video data corresponding to the target user, comprises:executing first preset processing on the multiple scanning images 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 human organ data set and a blood vessel data set, and the human organ data set comprises a plurality of human organ data corresponding to a plurality of human organs;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 the human organ data set and the blood vessel data set;and executing second preset processing aiming at the second medical image data to obtain the target 4D image.
- A VRDS 4D-based disease analysis device, comprising:the device comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring scanning images of a plurality of human organs of a target user to obtain a plurality of scanning images, and each human organ at least corresponds to one scanning image;the processing unit is used for processing the plurality of scanning images to obtain a target 4D image corresponding to the target user, wherein the target 4D image comprises target image data corresponding to the target user;the identification unit is used for identifying a disease part corresponding to the target user based on the target image data, wherein the disease part is one or more parts in the plurality of human organs;and the analysis unit is used for analyzing the disease condition of the diseased part to obtain a target disease condition analysis result.
- The apparatus according to claim 10, wherein if the target image data at least includes a blood vessel data set, the identifying unit is specifically configured to identify a corresponding diseased part aspect of the target user based on the target image data, and:selecting a plurality of target vessel data sets associated with the plurality of human organs based on the vessel data sets;obtaining a blood vessel central line data set corresponding to the plurality of target blood vessel data sets, wherein the blood vessel central line is a connecting line between each central point in the target blood vessel section;determining a vascularity data set corresponding to the plurality of human organs according to the blood vessel centerline data set, wherein each of the vascularity data sets corresponds to one of the human organs;determining a plurality of target organs from the vascularity data set, the plurality of target organs including at least one of: ear, nose and throat;acquiring a plurality of image data corresponding to the target organs in the target image set, wherein each target organ corresponds to one image data;and identifying the corresponding diseased part of the target user according to the plurality of image data.
- The apparatus according to claim 11, wherein in said identifying the diseased part corresponding to the target user according to the plurality of image data, the identifying unit is further configured to:generating a plurality of feature information corresponding to the target organs according to the image data, wherein the feature information comprises at least one of the following: color, shape, location, size;and identifying the disease part corresponding to the target user according to the characteristic information.
- The apparatus according to claim 11, wherein in said determining a plurality of target organs from said vascularity data set, said determining unit is specifically configured to:obtaining a blood vessel radius data set corresponding to the plurality of human organs in the blood vessel data set;determining a blood vessel distortion distribution data set corresponding to the blood vessel radius exceeding a preset threshold in the blood vessel distribution data set according to the blood vessel radius data set;determining the vessel distortion range from the vessel distortion distribution data set;determining the plurality of target organs from the vessel aberration distribution dataset and the vessel aberration range.
- The apparatus according to claim 13, wherein in said identifying the diseased sites in the target organs based on the feature information, the determining unit is further configured to:acquiring a preset diseased part identification model;inputting the plurality of characteristic information into the preset diseased part identification model to obtain a characteristic probability corresponding to each characteristic information in the plurality of characteristic information to obtain a plurality of characteristic probabilities;calculating at least one piece of feature information corresponding to the probability value exceeding a preset probability value;determining a diseased site in the plurality of target organs according to the at least one characteristic information.
- The apparatus according to any one of claims 10-14, wherein in said analyzing a condition for said disease site to obtain a target condition analysis result, said analyzing unit is further configured to:acquiring a plurality of groups of preset disease characteristic information corresponding to a preset disease characteristic database, wherein the preset disease characteristic database comprises a plurality of disease types, and each disease type corresponds to one group of preset disease characteristic information;matching a plurality of characteristic information corresponding to the disease part with the plurality of groups of preset disease characteristic information one by one to obtain a plurality of matching values, wherein each matching value corresponds to one disease type;acquiring a disease type corresponding to the maximum matching value in the plurality of matching values as a target disease type;and determining a target disease analysis result corresponding to the target user according to a preset mapping relation between the disease type and the disease analysis result.
- The apparatus according to claim 10, wherein in the aspect of identifying the diseased part corresponding to the target user based on the target image data, the identifying unit is further configured to:generating first target image data corresponding to any human organ i in the plurality of human organs according to the target image data, wherein the first target image data comprises at least k spatial position data, the k spatial position data corresponds to k first image data, and each spatial position data corresponds to one first image data, and k is a positive integer;and identifying the diseased part corresponding to the target user according to the k spatial position data.
- The apparatus according to claim 16, wherein in said identifying the target user's corresponding diseased part according to the k spatial position data, the identifying unit is further configured to:positioning the human organ i according to the k spatial position data;traversing the k first image data corresponding to the k spatial position data to obtain p abnormal first image data, wherein the parts corresponding to the p spatial position data corresponding to the p abnormal first image data are the disease parts, and p is an integer smaller than k.
- The apparatus according to claim 10, wherein in the processing of the plurality of scan images to obtain the target 4D video corresponding to the target user, if the target 4D video includes target video data corresponding to the target user, the processing unit is specifically configured to:executing first preset processing on the multiple scanning images 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 human organ data set and a blood vessel data set, and the human organ data set comprises a plurality of human organ data corresponding to a plurality of human organs;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 the human organ data set and the blood vessel data set;and executing second preset processing aiming at the second medical image data to obtain the target 4D image.
- 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.
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