Data systems, methods, apparatuses, and media for medical images

Abstract

The application discloses a kind of data systems, methods, equipment and media of medical image, wherein the data system of medical image includes database module, and medical image is stored;Analysis selection module selects analysis scheme based on preset analysis demand;ROI selection module is matched with corresponding ROI acquisition logic according to the selected analysis scheme;Analysis module is analyzed based on the ROI acquisition logic to the medical image, to obtain analysis result. By obtaining image data in database, and according to the analysis demand corresponding analysis scheme is selected, so that the analysis scheme can match the ROI acquisition logic, and then the ROI acquisition logic is analyzed based on medical image, to obtain analysis result, realize the whole process integration of data acquisition, parameter calculation, ROI acquisition, statistical analysis, effectively improve data processing capacity, improve user experience.

Description

Technical Field

[0001] This invention relates to the field of medical image processing technology, and in particular to a data system, method, device and medium for medical images. Background Technology

[0002] In modern medicine, medical imaging has undergone significant improvements. It is a non-invasive technique used to collect visual representations of the body's internal structures for clinical analysis and medical intervention. Medical imaging is widely used in the analysis of various clinical conditions.

[0003] However, existing image analysis systems still suffer from the problem of not being able to integrate different data, and their performance analysis is not yet perfect. For example, they cannot easily process, model, solve, and statistically analyze image data, resulting in cumbersome operation and making them unsuitable for medical researchers. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the above-mentioned defects in the prior art and provide a data system, method, device and medium for medical images.

[0005] The present invention solves the above-mentioned technical problems through the following technical solution:

[0006] This invention provides a medical image data system, the data system comprising:

[0007] The database module stores medical images;

[0008] The analysis selection module allows users to choose an analysis scheme based on preset analysis requirements.

[0009] The ROI selection module matches the corresponding ROI acquisition logic based on the selected analysis scheme.

[0010] The analysis module analyzes the medical image based on the ROI acquisition logic to obtain analysis results.

[0011] Preferably, the data system further includes:

[0012] The data filtering module filters the required medical images from the database module based on preset filtering tags to form at least one data set;

[0013] The data processing module matches different data groups with corresponding calculation models and calculation schemes, and obtains parameter graphs based on the calculation models and calculation schemes. The parameter graphs correspond to at least one analysis model.

[0014] The analysis module analyzes the corresponding parameters of the data set based on the parameter graph to obtain analysis results.

[0015] Preferably, the analysis scheme includes at least one of consistency analysis, correlation analysis, variance analysis, and parametric statistics.

[0016] Preferably, when the analysis scheme is a consistency analysis;

[0017] The ROI selection module matches the corresponding first ROI acquisition logic based on the consistency analysis.

[0018] The analysis module performs consistency analysis on at least one set of data groups based on the first ROI acquisition logic to obtain a first analysis result.

[0019] The consistency analysis includes analyzing different ROIs within at least one of the data groups or analyzing the same ROI between at least two of the data groups.

[0020] Preferably, when the analysis scheme is a correlation analysis;

[0021] The ROI selection module matches the corresponding second ROI acquisition logic based on the correlation analysis.

[0022] The analysis module performs correlation analysis on at least one set of data groups based on the second ROI acquisition logic and preset parameters to obtain a second analysis result.

[0023] Preferably, when the analysis scheme is an analysis of variance;

[0024] The ROI selection module matches the corresponding third ROI acquisition logic based on the variance analysis.

[0025] The analysis module performs correlation analysis on at least two sets of data groups based on the third ROI acquisition logic to obtain the third analysis result.

[0026] And / or,

[0027] When the analysis scheme is parameter statistics;

[0028] The ROI selection module uses the parameters to statistically match the corresponding fourth ROI acquisition logic.

[0029] The analysis module performs parameter statistics on at least one set of data groups based on the fourth ROI acquisition logic to obtain the fourth analysis result.

[0030] Preferably, the database module further includes a personal database and / or a general database and / or a database to be imported;

[0031] And / or,

[0032] The data processing module determines whether the data in the personal database and / or the overall data database already has a parameter graph. If so, it prompts whether to reuse it.

[0033] And / or,

[0034] The data processing module includes a preprocessing unit;

[0035] The preprocessing unit performs preprocessing operations on the analysis model.

[0036] And / or,

[0037] The data system also includes:

[0038] The results display module shows the analysis results and synchronizes them to the personal database.

[0039] The present invention also provides a data analysis method for medical images, the data analysis method being implemented using the medical image data system described above, the method comprising:

[0040] Build a database that stores medical images;

[0041] Select an analysis scheme based on pre-defined analysis requirements;

[0042] The corresponding ROI acquisition logic is matched according to the selected analysis scheme;

[0043] The medical image is analyzed based on the ROI acquisition logic to obtain analysis results.

[0044] Preferably, the method further includes:

[0045] The required medical images are selected from the database module based on preset filtering tags to form at least one set of data.

[0046] The different data sets are matched with corresponding calculation models and calculation schemes, and parameter graphs are obtained based on the calculation models and calculation schemes. The parameter graphs correspond to at least one analysis model.

[0047] The parameters of the data set are analyzed based on the parameter graph to obtain the analysis results.

[0048] Preferably, the analysis scheme includes at least one of consistency analysis, correlation analysis, variance analysis, and parametric statistics.

[0049] Preferably, when the analysis scheme is a consistency analysis;

[0050] The logic for matching the corresponding ROI based on the selected analysis scheme includes:

[0051] The first ROI acquisition logic is matched according to the consistency analysis;

[0052] The analysis of the medical image based on the ROI acquisition logic to obtain analysis results includes:

[0053] Based on the first ROI acquisition logic, a consistency analysis is performed on at least one set of the data groups to obtain a first analysis result;

[0054] The consistency analysis includes analyzing different ROIs within at least one of the data groups or analyzing the same ROI between at least two of the data groups.

[0055] Preferably, when the analysis scheme is a correlation analysis;

[0056] The logic for matching the corresponding ROI based on the selected analysis scheme includes:

[0057] The corresponding second ROI acquisition logic is matched based on the correlation analysis.

[0058] The analysis of the medical image based on the ROI acquisition logic to obtain analysis results includes:

[0059] Based on the second ROI acquisition logic and preset parameters, correlation analysis is performed on at least one set of the data groups to obtain the second analysis result.

[0060] Preferably, when the analysis scheme is an analysis of variance;

[0061] The logic for matching the corresponding ROI based on the selected analysis scheme includes:

[0062] The logic for obtaining the corresponding third ROI is based on the variance analysis;

[0063] The analysis of the medical image based on the ROI acquisition logic to obtain analysis results includes:

[0064] Based on the third ROI acquisition logic, correlation analysis is performed on at least two sets of data groups to obtain the third analysis results;

[0065] And / or,

[0066] When the analysis scheme is parameter statistics;

[0067] The logic for matching the corresponding ROI based on the selected analysis scheme includes:

[0068] The corresponding fourth ROI acquisition logic is obtained based on the parameters.

[0069] The analysis of the medical image based on the ROI acquisition logic to obtain analysis results includes:

[0070] Based on the fourth ROI acquisition logic, parameter statistics are performed on at least one set of the data groups to obtain the fourth analysis result.

[0071] Preferably, the database further includes a personal database and / or a general database and / or a database to be imported;

[0072] And / or,

[0073] The method further includes:

[0074] Determine whether the data in the personal database and / or the overall database already has a parameter graph; if so, prompt whether to reuse it.

[0075] And / or,

[0076] The method further includes:

[0077] Display the analysis results and synchronize them to your personal database.

[0078] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein the processor executes the computer program to implement the data analysis method for medical images as described above.

[0079] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data analysis method for medical images as described above.

[0080] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this application.

[0081] The positive and progressive effects of this invention are as follows: by acquiring image data from the database and selecting the corresponding analysis scheme according to the analysis requirements, the analysis scheme can match the corresponding ROI acquisition logic, and then the medical images are analyzed based on the ROI acquisition logic to obtain the analysis results. This realizes the integration of the entire process of data acquisition, parameter calculation, ROI acquisition, and statistical analysis, effectively improving data processing capabilities and enhancing the user experience. Attached Figure Description

[0082] Figure 1 A schematic diagram of the modules of a medical image data system provided in an embodiment of the present invention.

[0083] Figure 2 This is a schematic diagram of the first process of a data analysis method for medical images provided in an embodiment of the present invention.

[0084] Figure 3 This is a schematic diagram of the second process of the data analysis method for medical images provided in an embodiment of the present invention.

[0085] Figure 4 This is a schematic diagram of the third process of the data analysis method for medical images provided in an embodiment of the present invention.

[0086] Figure 5 This is a schematic diagram of the fourth process of the data analysis method for medical images provided in an embodiment of the present invention.

[0087] Figure 6 This is a schematic diagram of the fifth process of the data analysis method for medical images provided in an embodiment of the present invention.

[0088] Figure 7 This is a schematic diagram of the structure of an electronic device for implementing a data analysis method for medical images, as provided in an embodiment of the present invention. Detailed Implementation

[0089] The present invention will be further illustrated by way of embodiments below, but the present invention is not limited to the scope of the embodiments described herein.

[0090] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0091] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0092] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0093] As indicated in this specification, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0094] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0095] Existing image analysis systems suffer from limitations such as the inability to integrate diverse data and imperfect performance analysis. For example, they lack convenient methods for image data processing, modeling, solving, and statistical analysis, leading to cumbersome operations. Therefore, this embodiment provides a medical image data system. Figure 1 As shown, the medical image data system of this embodiment includes:

[0096] Database module 1 stores medical images.

[0097] As an optional implementation of this embodiment, the database module 1 also includes at least one of a personal database, a general database, and a database to be imported. The personal database can store historical data and results owned by an individual, and an application can be made to synchronize the personal database to the general database. The general database has more data and results.

[0098] It should be noted that the database module 1 in this embodiment stores medical images, which can be acquired by medical imaging equipment. Optionally, medical imaging equipment includes computed tomography (CT), magnetic resonance imaging (MRI), X-ray imaging (XR), molecular imaging, ultrasound equipment, and multimodal medical imaging equipment and combinations thereof.

[0099] Optionally, the database may not only store medical images, but also corresponding processing results, logic, status, etc. For example, it may store preprocessing parameters, ROI, parameter graphs, statistical results, etc. This embodiment does not limit this.

[0100] In addition, database module 1 also stores the patient's basic information, diagnosis and treatment status, treatment plan, tags for subsequent processing, and the save path of processing files, etc. It should also be noted that in this embodiment, the acquisition, storage and application of the user's personal information all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0101] Analysis Selection Module 2 allows you to select an analysis scheme based on preset analysis requirements.

[0102] The analysis selection module 2 allows users to select the analysis scheme they need based on preset analysis requirements. Each specific analysis requirement category includes multiple data analysis items, from which users can select the data analysis items they want to analyze. Different analysis scenario categories may include the same data analysis items.

[0103] As an optional implementation of this embodiment, the analysis scheme includes at least one of consistency analysis, correlation analysis, variance analysis, and parametric statistics.

[0104] ROI selection module 3 matches the corresponding ROI acquisition logic based on the selected analysis scheme.

[0105] Different analysis schemes will activate different Region of Interest (ROI) acquisition logics. It should be noted that if the database contains the corresponding ROI of the medical image, it can be imported from the database, thus eliminating the need for re-acquisition. Depending on the analysis target, ROI acquisition can segment organs and tissues, identify lesion areas, and automatically symmetric regions. In addition, users can also supplement ROI acquisition through manual import and manual editing, thereby enriching the ways users obtain ROI regions and improving operational efficiency.

[0106] Additionally, it should be noted that there are multiple ways to acquire the ROI of medical images, such as using at least one of Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN).

[0107] Analysis module 4 analyzes medical images based on ROI acquisition logic to obtain analysis results.

[0108] This embodiment acquires image data from the database and selects the corresponding analysis scheme according to the analysis requirements, so that the analysis scheme can match the corresponding ROI acquisition logic. Then, it analyzes the medical images based on the ROI acquisition logic to obtain the analysis results. This realizes the integration of the entire process of data acquisition, parameter calculation, ROI acquisition and statistical analysis, effectively improving data processing capabilities and enhancing the user experience.

[0109] As an optional implementation of this embodiment, the medical image data system of this embodiment further includes:

[0110] Data filtering module 5 filters the required medical images from database module 1 based on preset filtering tags to form at least one set of data.

[0111] Optionally, preset filter tags include, but are not limited to, general information such as age, gender, and disease.

[0112] By using filter tags, you can select the desired dataset (medical images) from the overall database, individual databases, and the data imported in this instance, forming at least one data set, which facilitates subsequent analysis. It should also be noted that data without preset filter tags can be corrected by adding new tags.

[0113] Data processing module 6 matches different data sets with corresponding calculation models and calculation schemes, and obtains parameter maps based on the calculation models and calculation schemes. The parameter maps correspond to at least one analysis model.

[0114] Optionally, the analysis model includes, but is not limited to, at least one of DWI (Diffusion Weighted Imaging), IVIM (Intro-voxel Incoherent Movement), DKI (Diffusion Kurtosis Imaging), and DTI (Diffusion Tensor Imaging).

[0115] As an optional implementation of this embodiment, the data processing module 6 includes a preprocessing unit 61.

[0116] Preprocessing unit 61 performs preprocessing operations on the analysis model.

[0117] The preprocessing unit 61 performs preprocessing operations on the analysis model, such as filtering, motion correction, and spatial standardization. Optionally, the database stores filtering parameters and results, motion correction algorithm parameters and results, template types, and standardization results.

[0118] As an optional implementation of this embodiment, the data processing module 6 determines whether the data in the personal database and / or the general database already has a parameter graph. If so, it prompts whether to reuse the data, thus avoiding repeated analysis of the data and effectively reducing workload.

[0119] Analysis module 4 analyzes the corresponding parameters of the data set based on the parameter graph to obtain analysis results.

[0120] By performing statistical analysis on the same parameter on different populations, we can see if there are statistical differences. For a certain parameter, we can select certain factors, such as gender, age, health status, and other parameters, and thus know the correlation between the parameter and the above factors.

[0121] This implementation can also filter the required medical images from the database module according to the category of the analysis scheme to form multiple data groups for analysis. This avoids the original limitation of only being able to analyze single data and not being able to analyze large batches of data, making the analysis results more representative and regular.

[0122] As an optional implementation method of this embodiment, when the analysis scheme is consistency analysis.

[0123] ROI selection module 3 uses consistency analysis to match the corresponding first ROI acquisition logic.

[0124] Analysis module 4 performs consistency analysis on at least one set of data based on the first ROI acquisition logic to obtain the first analysis result.

[0125] Consistency analysis includes analyzing different ROIs within at least one data group or analyzing the same ROI between at least two data groups. In other words, consistency analysis can be analyzing the same ROI between at least two data groups, or it can be analyzing different ROIs within one data group.

[0126] Optionally, for consistency analysis, a portion of the samples can be set as the control group and the remaining portion as the experimental group. The system can then automatically obtain consistency results for different ROIs and different parameter plots between the two groups. Consistency analysis can include ICC analysis, kappa analysis, etc.

[0127] As an optional implementation method of this embodiment, when the analysis scheme is correlation analysis.

[0128] ROI selection module 3 uses correlation analysis to match the corresponding second ROI acquisition logic.

[0129] Analysis module 4 performs correlation analysis on at least one set of data based on the second ROI acquisition logic and preset parameters to obtain the second analysis results.

[0130] Optionally, for correlation analysis, users can select certain parameters (which can be calculated parameters or label values) as independent variables and certain parameters as dependent variables to statistically analyze the correlation between them.

[0131] As an optional implementation method of this embodiment, when the analysis scheme is analysis of variance.

[0132] ROI selection module 3 uses the logic of matching the corresponding third ROI based on variance analysis.

[0133] Analysis module 4 performs correlation analysis on at least two sets of data based on the third ROI acquisition logic to obtain the third analysis results.

[0134] Optionally, for analysis of variance, a portion of the samples can be set as the control group and a portion as the experimental group to analyze whether there are significant differences in parameters within the same ROI region; alternatively, different ROIs can be set as different analysis groups, such as diseased areas and normal areas, to determine whether there are significant differences in statistical parameters. At the same time, different groups can be set up to collect information such as the mean, variance, and sample size of each parameter between each group.

[0135] As an optional implementation method of this embodiment, when the analysis scheme is parameter statistics.

[0136] ROI selection module 3 uses the parameter statistics to match the corresponding fourth ROI acquisition logic.

[0137] Analysis module 4 performs parameter statistics on at least one set of data based on the fourth ROI acquisition logic to obtain the fourth analysis results.

[0138] As an optional implementation of this embodiment, the data system of this embodiment further includes:

[0139] Result Display Module 7 displays the analysis results and synchronizes them to the individual database.

[0140] The statistical analysis results are displayed in charts and graphs and can also be saved as corresponding text formats. This provides an intuitive and clear presentation of the analysis results, making it easier for engineers to summarize and generalize the relevant features of medical images based on the analysis results, thereby improving the efficiency of medical image analysis and processing.

[0141] This embodiment provides a variety of different analysis schemes. Users can choose the corresponding analysis scheme according to their own analysis needs. Then, the system can automatically analyze large batches of data by adopting the corresponding ROI acquisition logic according to the analysis scheme. At the same time, the introduction of databases at different levels can not only expand the data coverage, but also reduce unnecessary repetitive operations, effectively improve data processing capabilities, and enhance the user experience.

[0142] For modules such as models and preprocessing, users can write corresponding programs based on system specifications and mount them to the system as plugins. After mounting, the operation logic is consistent with the built-in functions.

[0143] It should also be noted that the medical image data system of this embodiment can be, for example, a separate chip, chip module, or electronic device, or a chip or chip module integrated within an electronic device. Regarding the modules / units included in the various devices and products described in the above embodiments, they can be software modules / units, hardware modules / units, or a combination of both. For example, for various devices and products applied to or integrated into a chip, all included modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs running on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits; for various devices and products applied to or integrated into a chip module, all included modules / units can be implemented using hardware methods such as circuits, and different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The components can be implemented using software programs that run on the processor integrated within the chip module. The remaining (if any) modules / units can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into the terminal, each of its components / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on the processor integrated within the terminal, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits.

[0144] Corresponding to the medical image data system described above, this embodiment also provides a medical image data analysis method. The data analysis method of this embodiment utilizes the medical image data system described above, such as... Figure 2 As shown, the data analysis of the medical images in this embodiment includes:

[0145] S101. Construct a database that stores medical images.

[0146] As an optional implementation of this embodiment, the database also includes at least one of a personal database, a general database, and a database to be imported. The personal database can store historical data and results owned by an individual, and an application can be made to synchronize the personal database to the general database, which has more data and results.

[0147] It should be noted that the database in this embodiment stores medical images, which can be acquired through medical imaging equipment. Optionally, medical imaging equipment includes computed tomography (CT) scanners, magnetic resonance imaging (MRI) scanners, X-ray imaging equipment, molecular imaging equipment, ultrasound equipment, and multimodal medical imaging equipment combinations thereof.

[0148] Optionally, the database may not only store medical images, but also corresponding processing results, logic, status, etc. For example, it may store preprocessing parameters, ROI, parameter graphs, statistical results, etc. This embodiment does not limit this.

[0149] In addition, the database also stores the patient's basic information, diagnosis and treatment status, treatment plan, follow-up processing, and the file storage path, etc. It should also be noted that in this embodiment, the acquisition, storage and application of the user's personal information comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0150] S102. Select an analysis scheme based on preset analysis requirements.

[0151] Users can select the analysis plan they need based on preset analysis requirements. Each specific analysis requirement category includes multiple data analysis items, from which users can select the data analysis items they want to analyze. Different analysis scenario categories may include the same data analysis items.

[0152] As an optional implementation of this embodiment, the analysis scheme includes at least one of consistency analysis, correlation analysis, variance analysis, and parametric statistics.

[0153] S103. Match the corresponding ROI acquisition logic according to the selected analysis scheme.

[0154] Different analysis schemes will activate different Region of Interest (ROI) acquisition logics. It should be noted that if there are corresponding ROIs for medical images in the database, they can be imported from the database, thus eliminating the need for acquisition again. Depending on the analysis target, ROI acquisition can segment organs and tissues, identify lesion areas, and automatically symmetric regions. In addition, users can also supplement ROI acquisition by manually importing and editing, thereby enriching the ways users obtain ROI regions and improving operational efficiency.

[0155] Additionally, it should be noted that there are multiple ways to acquire the ROI of medical images, such as using at least one of Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN).

[0156] S104. Analyze the medical image based on the ROI acquisition logic to obtain the analysis results.

[0157] This embodiment acquires image data from the database and selects the corresponding analysis scheme according to the analysis requirements, so that the analysis scheme can match the corresponding ROI acquisition logic. Then, it analyzes the medical images based on the ROI acquisition logic to obtain the analysis results. This realizes the integration of the entire process of data acquisition, parameter calculation, ROI acquisition and statistical analysis, effectively improving data processing capabilities and enhancing the user experience.

[0158] As an optional implementation of this embodiment, the medical image data analysis method of this embodiment further includes:

[0159] S201. Select the required medical images from the database based on preset filter tags to form at least one set of data.

[0160] Optionally, preset filter tags include, but are not limited to, general information such as age, gender, and disease.

[0161] By using filter tags, you can select the desired dataset (medical images) from the overall database, individual databases, and the data imported in this instance, forming at least one data set, which facilitates subsequent analysis. It should also be noted that data without preset filter tags can be corrected by adding new tags.

[0162] S202. Match different data sets with corresponding calculation models and calculation schemes, and obtain parameter maps based on the calculation models and calculation schemes. The parameter maps correspond to at least one analysis model.

[0163] Optionally, the analysis model includes, but is not limited to, at least one of DWI, IVIM, DKI, and DTI.

[0164] The analysis model is preprocessed, such as by filtering, motion correction, and spatial standardization. Optionally, the database stores the filtering parameters and results, the motion correction algorithm parameters and results, the template type, and the standardization results.

[0165] As an optional implementation of this embodiment, step S202 further includes:

[0166] S2021. Determine whether the data in the personal database and / or the general database already has a parameter graph. If so, prompt whether to reuse it, thereby avoiding duplicate data analysis and effectively reducing workload.

[0167] S203. Analyze the corresponding parameters of the data set based on the parameter diagram to obtain the analysis results.

[0168] By performing statistical analysis on the same parameter on different populations, we can see if there are statistical differences. For a certain parameter, we can select certain factors, such as gender, age, health status, and other parameters, and thus know the correlation between the parameter and the above factors.

[0169] This implementation method can also filter the required medical images from the database according to the category of the analysis scheme to form multiple data groups for analysis. This avoids the original limitation of only being able to analyze single data and not being able to analyze large batches of data, making the analysis results more representative and regular.

[0170] As an optional implementation method of this embodiment, such as Figure 3 As shown, this applies when the analysis scheme is a consistency analysis.

[0171] Step S103 includes:

[0172] S1031. Based on the consistency analysis, match the corresponding first ROI acquisition logic.

[0173] Step S104 includes:

[0174] S1041. Perform consistency analysis on at least one set of data based on the first ROI acquisition logic to obtain the first analysis result.

[0175] Consistency analysis includes analyzing different ROIs within at least one data group or analyzing the same ROI between at least two data groups. In other words, consistency analysis can be analyzing the same ROI between at least two data groups, or it can be analyzing different ROIs within one data group.

[0176] Optionally, for consistency analysis, a portion of the samples can be set as the control group and the remaining portion as the experimental group. The system can then automatically obtain consistency results for different ROIs and different parameter plots between the two groups. Consistency analysis can include ICC analysis, kappa analysis, etc.

[0177] As an optional implementation method of this embodiment, such as Figure 4 As shown, this applies when the analysis scheme is correlation analysis.

[0178] Step S103 includes:

[0179] S1032. Based on the correlation analysis, match the corresponding second ROI acquisition logic.

[0180] Step S104 includes:

[0181] S1042. Based on the second ROI acquisition logic and preset parameters, perform correlation analysis on at least one set of data to obtain the second analysis results.

[0182] Optionally, for correlation analysis, users can select certain parameters (which can be calculated parameters or label values) as independent variables and certain parameters as dependent variables to statistically analyze the correlation between them.

[0183] As an optional implementation method of this embodiment, such as Figure 5 As shown, this applies when the analysis scheme is an analysis of variance.

[0184] Step S103 includes:

[0185] S1033, Obtain the corresponding third ROI based on the variance analysis.

[0186] Step S104 includes:

[0187] S1043. Based on the third ROI acquisition logic, perform correlation analysis on at least two sets of data to obtain the third analysis results.

[0188] Optionally, for analysis of variance, a portion of the samples can be set as the control group and a portion as the experimental group to analyze whether there are significant differences in parameters within the same ROI region; alternatively, different ROIs can be set as different analysis groups, such as diseased areas and normal areas, to determine whether there are significant differences in statistical parameters. At the same time, different groups can be set up to collect information such as the mean, variance, and sample size of each parameter between each group.

[0189] As an optional implementation method of this embodiment, such as Figure 6 As shown, this applies when the analysis scheme is parametric statistics.

[0190] Step S103 includes:

[0191] S1034. Based on the parameter statistics, match the corresponding fourth ROI acquisition logic.

[0192] Step S104 includes:

[0193] S1044. Perform parameter statistics on at least one set of data based on the fourth ROI acquisition logic to obtain the fourth analysis results.

[0194] As an optional implementation of this embodiment, the invention further includes:

[0195] S105. Display the analysis results and synchronize them to the personal database.

[0196] The statistical analysis results are displayed in charts and graphs and can also be saved as corresponding text formats. This provides an intuitive and clear presentation of the analysis results, making it easier for engineers to summarize and generalize the relevant features of medical images based on the analysis results, thereby improving the efficiency of medical image analysis and processing.

[0197] This embodiment provides a variety of different analysis schemes. Users can choose the corresponding analysis scheme according to their own analysis needs. Then, the system can automatically analyze large batches of data by adopting the corresponding ROI acquisition logic according to the analysis scheme. At the same time, the introduction of databases at different levels can not only expand the data coverage, but also reduce unnecessary repetitive operations, effectively improve data processing capabilities, and enhance the user experience.

[0198] Figure 7 This is a schematic diagram of the structure of an electronic device provided in this embodiment. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in the above embodiment. Figure 7 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0199] like Figure 7 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0200] Bus 33 includes a data bus, an address bus, and a control bus.

[0201] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0202] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0203] The processor 31 executes various functional applications and data processing, such as the method described above, by running computer programs stored in the memory 32.

[0204] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 7As shown, network adapter 36 communicates with other modules of the model-generated device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0205] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0206] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method as described in the above embodiments.

[0207] The readable storage medium may be more specifically, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0208] In a possible implementation, the present invention can also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the method described above.

[0209] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0210] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A medical image data system, characterized in that, The data system includes: The database module stores medical images; The analysis selection module allows users to choose an analysis scheme based on preset analysis requirements. The ROI selection module matches the corresponding ROI acquisition logic based on the selected analysis scheme. The analysis module analyzes the medical image based on the ROI acquisition logic to obtain analysis results; The analysis scheme includes at least one of consistency analysis, correlation analysis, variance analysis, and parametric statistics. Different analysis schemes correspond to different ROI acquisition logics. The data system also includes: The data filtering module filters the required medical images from the database module based on preset filtering tags to form at least one data set; The data processing module matches different data groups with corresponding calculation models and calculation schemes, and obtains parameter graphs based on the calculation models and calculation schemes. The parameter graphs correspond to at least one analysis model. The analysis module specifically analyzes the corresponding parameters of the data set based on the parameter graph to obtain the analysis results.

2. The medical image data system as described in claim 1, characterized in that, When the analysis scheme is a consistency analysis; The ROI selection module matches the corresponding first ROI acquisition logic based on the consistency analysis. The analysis module performs consistency analysis on at least one set of data groups based on the first ROI acquisition logic to obtain a first analysis result. The consistency analysis includes analyzing different ROIs within at least one of the data groups or analyzing the same ROI between at least two of the data groups.

3. The medical image data system as described in claim 1, characterized in that, When the analysis scheme is a correlation analysis; The ROI selection module matches the corresponding second ROI acquisition logic based on the correlation analysis. The analysis module performs correlation analysis on at least one set of data groups based on the second ROI acquisition logic and preset parameters to obtain a second analysis result.

4. The medical image data system as described in claim 1, characterized in that, When the analysis scheme is an analysis of variance; The ROI selection module matches the corresponding third ROI acquisition logic based on the variance analysis. The analysis module performs variance analysis on at least two sets of data based on the third ROI acquisition logic to obtain the third analysis result.

5. The medical image data system as described in claim 1, characterized in that, When the analysis scheme is parameter statistics; The ROI selection module uses the parameters to statistically match the corresponding fourth ROI acquisition logic. The analysis module performs parameter statistics on at least one set of data groups based on the fourth ROI acquisition logic to obtain the fourth analysis result.

6. The medical image data system as described in claim 1, characterized in that, The database module also includes a personal database and / or a general database and / or a database to be imported; The data processing module determines whether the data in the personal database and / or the overall data database already has a parameter graph. If so, it prompts whether to reuse it. The data processing module includes a preprocessing unit; The preprocessing unit performs preprocessing operations on the analysis model. The data system also includes: The results display module shows the analysis results and synchronizes them to the personal database.

7. A data analysis method for medical images, characterized in that, The data analysis method is implemented using the medical image data system as described in any one of claims 1-6, and the method includes: Build a database that stores medical images; Select an analysis scheme based on pre-defined analysis requirements; The corresponding ROI acquisition logic is matched according to the selected analysis scheme; The medical image is analyzed based on the ROI acquisition logic to obtain analysis results; The analysis scheme includes at least one of consistency analysis, correlation analysis, variance analysis, and parametric statistics. Different analysis schemes correspond to different ROI acquisition logics. The method further includes: The required medical images are selected from the database based on preset filtering tags to form at least one set of data. The different data sets are matched with corresponding calculation models and calculation schemes, and parameter graphs are obtained based on the calculation models and calculation schemes. The parameter graphs correspond to at least one analysis model. The parameters of the data set are analyzed based on the parameter graph to obtain the analysis results.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes a computer program, it implements the data analysis method for medical images as described in claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the data analysis method for medical images as described in claim 7.

Images