MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning

Abstract

The invention discloses an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning, and relates to the technical field of auxiliary medical treatment based on deep learning. The platform comprises a user login module, a front-end monitoring module, a user demand analysis module, a data loading and preprocessing module, a reasoning module, a first display module, a second display module, a diagnosis module, a storage module, a query module and an interaction module. The platform is combined with a 3D data visualization library, 3D dynamic display of the hippocampus is achieved, the structure of the hippocampus can be observed more comprehensively and detailedly, and data can be deeply known from different angles and layers through interactive operation. According to the invention, a user-friendly operation interface is provided, and even if the user does not contact deep learning, the data can be easily uploaded and the segmentation work can be quickly started. The simple and visual design reduces the learning cost of the user and improves the working efficiency.

Description

Technical Field

[0001] The present application relates to the field of auxiliary medical technology based on deep learning, and in particular to an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning. Background Art

[0002] The hippocampus, as a key component of the brain structure, carries important responsibilities for cognitive functions such as memory and learning. In medical research and clinical practice, accurate segmentation of the hippocampus is particularly important, and it has an important significance for studying brain diseases and diagnosing neurological diseases. However, most traditional hippocampal segmentation methods rely on manual operations by doctors, which is not only time-consuming and labor-intensive, but also easily affected by the doctor's subjective factors, resulting in errors and instability in the segmentation results. In view of this, designing a software that can automatically segment the hippocampus with the help of deep learning technology has become a hot topic and difficulty in current research. This software can use advanced deep learning frameworks to perform efficient and accurate automatic segmentation of brain images, greatly improving the accuracy and efficiency of segmentation. At the same time, due to the standardization and objectivity of the software's operation process, it can also effectively reduce the interference of human factors and improve the consistency and reliability of segmentation results. In addition, the software for automatic segmentation of the hippocampus has a wide range of application prospects. It can not only be used in medical research and clinical practice to provide strong support for the early detection, diagnosis and treatment of brain diseases; it can also be used in research in the field of neuroscience to provide important data support for in-depth understanding of the physiological functions and mechanisms of the hippocampus. In summary, designing a software that can automatically segment the hippocampus is of great significance. It will provide new technical means for the research and diagnosis of brain diseases and promote the progress and development of the medical field. Summary of the invention

[0003] The invention purpose of this application is to develop a 3D intelligent analysis and diagnosis platform for MRI hippocampus based on deep learning, so as to realize automatic segmentation of the brain hippocampus region in MRI data, and visualize the 3D and 2D segmentation results of the hippocampus, and also provide hippocampus volume analysis and diagnosis functions, as well as historical record query functions. It is to provide doctors with a convenient and efficient auxiliary diagnosis tool to help them better perform medical diagnosis related to the hippocampus.

[0004] The technical solution adopted by the present invention is: a 3D intelligent analysis and diagnosis platform of MRI hippocampus based on deep learning, a B / S architecture platform based on a Web framework, including: a user login module, a front-end monitoring module, a user demand analysis module, a data loading and preprocessing module, a reasoning module, a first display module, a second display module, a diagnosis module, a storage module, a query module and an interaction module;

[0005] Among them, the user login module is used to implement browser-based user registration / and user login operations; if the current user is registering, the user login module stores the current user's registration information in the background database through the storage module;

[0006] The front-end monitoring module monitors the user needs initiated by the user who logs in through the browser; and sends the monitored user needs to the back-end user demand parsing module;

[0007] The user demand analysis module is used to analyze the received user demand, extract the user demand, and match the corresponding user operation item based on the keywords in the user demand, and then map and link the corresponding operation web page based on the user operation item, so as to display the operation web page to the currently logged-in user through the browser;

[0008] Among them, user operation items include but are not limited to: data loading and preprocessing, model reasoning, diagnosis, result viewing, data retrieval and interaction;

[0009] The background processing module corresponding to the user operation item of data loading and preprocessing is the data loading and preprocessing module, which sends relevant prompt information of the loading data format to the currently logged-in user through the browser; when the front-end monitoring module monitors the user loading the corresponding data file, the currently loaded data file is used as the source data of the hippocampus segmentation of the current user and sent to the data loading and preprocessing module;

[0010] The data loading and preprocessing module preprocesses the received source data of hippocampal segmentation according to the established data preprocessing format so that the source data matches the input of the segmentation model preset in the inference module; and then stores the preprocessed source data in the specified table item of the storage module;

[0011] The background processing module corresponding to the user operation item of model inference is the inference module, and a trained segmentation model is pre-installed in the inference module. When the front-end monitoring module monitors that the user's current user operation item is model inference, the most recent loaded data file of the current user is read from the storage module, and input into the segmentation model to perform forward reasoning operation to obtain the hippocampus segmentation result; at the same time, the hippocampus segmentation result is stored in the specified table item of the storage module, wherein the output form of the hippocampus segmentation result includes two-dimensional and three-dimensional forms; and by calling the first display module, the source data of the hippocampus segmentation and the hippocampus segmentation result are visually output to the user for three-dimensional display, and the hippocampus segmentation result is displayed in two dimensions with different sections;

[0012] The background processing module corresponding to the user operation item of diagnosis is the diagnosis module, which is used for users (mainly medical personnel) to input patient information, and for the diagnosis doctor to enter information describing and diagnosing the hippocampus segmentation results;

[0013] The background processing module corresponding to the user operation item of result viewing is a second display module, which is used to display the source data of hippocampal segmentation and the hippocampal segmentation results in three dimensions, and to display the hippocampal segmentation results in two dimensions with different sections;

[0014] The backend processing module corresponding to the user operation item of data retrieval is a query module, which is used to read the content matching the retrieval demand from the storage module based on the retrieval demand monitored by the front-end monitoring module and realize the visual output display of the retrieval through the second display module;

[0015] The background processing module corresponding to the interactive user operation item is an interactive module, which is used to provide the user with a display selection operation of optional display content and form of the result viewing content.

[0016] Furthermore, the display selection operations provided by the interaction module include: rotation, translation and / or color change.

[0017] Furthermore, the search content visualized by the query module through the second display module includes but is not limited to: hippocampus segmentation results, hippocampus volume, 2D maximum area, patient information, etc.

[0018] Furthermore, the interactive functions provided by the interactive module also include: mouse hover zoom and page turning functions.

[0019] Furthermore, the segmentation model preset in the model inference is a segmentation model based on the nnU-Net model.

[0020] Furthermore, both the first display module and the second display module realize three-dimensional display through the 3D data visualization library tool vtk.js.

[0021] Based on the nnU-Net framework, this application has developed a software platform for automatically segmenting the hippocampus region in brain MRI images. The platform can process MRI brain data uploaded by users and realize automatic segmentation of the hippocampus through a pre-trained hippocampus segmentation model. In addition, the platform also has the function of displaying 3D and 2D segmentation results, allowing users to intuitively view the segmentation effect. In order to improve the continuity and reference of diagnosis, the platform also designs a historical record query function, allowing users to trace and compare past segmentation results. At the same time, in order to ensure the performance and accuracy of the model, the platform can also provide real-time feedback on model performance indicators. Combining these functions, the present invention provides doctors with an efficient auxiliary diagnosis tool, which aims to improve the diagnostic efficiency and accuracy of hippocampus-related diseases.

[0022] Compared with the prior art, the technical solution provided by this application brings at least the following beneficial effects:

[0023] (1) 3D dynamic display technology: This application can be combined with vtk.js, a powerful 3D data visualization library, to achieve 3D dynamic display of the hippocampus. This display method can not only observe the structure of the hippocampus in a more comprehensive and detailed manner, but also enable in-depth understanding of the data from different angles and levels through interactive operations. vtk.js supports a variety of common grid representations and allows advanced rendering features such as volume rendering, making the display of 3D models more vivid and accurate.

[0024] (2) High-precision segmentation technology: This application uses nn-Unet as the basic segmentation framework, which is a deep learning model widely used in the field of medical image segmentation. nn-Unet has achieved excellent results in multiple medical image segmentation competitions. It is highly versatile, effective, and easy to use. On the platform of this application, this technology is used to achieve high-precision automatic segmentation of the hippocampus, ensuring the high accuracy of the segmentation results.

[0025] (3) User-friendly interface: This application is committed to creating a simple and easy-to-use interface. Even users who have never been exposed to deep learning can easily upload data and quickly start segmentation. This simple and intuitive design reduces the user's learning cost and improves work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0027] Figure 1 An overview of the technical route of a deep learning-based MRI hippocampus 3D intelligent analysis and diagnosis platform provided in the embodiments of the present application;

[0028] Figure 2 A front-end and back-end development process of a deep learning-based MRI hippocampus 3D intelligent analysis and diagnosis platform provided in an embodiment of the present application;

[0029] Figure 3 A data interaction process between the home page and the result viewing page of an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning provided in an embodiment of the present application;

[0030] Figure 4 A data interaction process for an indicator verification page of an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning provided in an embodiment of the present application;

[0031] Figure 5 A 2D visualization flowchart provided for an embodiment of the present application;

[0032] Figure 6A process diagram of the find_max_slice function in the 2D visualization process provided in an embodiment of the present application;

[0033] Figure 7 A comparison of data before and after enhancement of an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning provided in an embodiment of the present application;

[0034] Figure 8 An example of homepage segmentation of a deep learning-based MRI hippocampus 3D intelligent analysis and diagnosis platform provided in an embodiment of the present application;

[0035] Fig. 9 A result viewing page interface of an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning provided in an embodiment of the present application;

[0036] Fig.10 An indicator verification interface of an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning provided in an embodiment of the present application;

[0037] Fig.11 A hippocampus knowledge popularization interface of an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions of the embodiments of the present application will be described in detail and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described with reference to the drawings are exemplary and are intended to be used to explain the present application, and cannot be understood as limiting the present application.

[0039] The embodiment of the present application provides an MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning, including: a user login module, a front-end monitoring module, a user demand analysis module, a data loading and preprocessing module, an inference module, a first display module, a second display module, a diagnosis module, a storage module, a query module and an interaction module; wherein the user login module is used to implement browser-based user registration / and user login operations; if the current user is registered, the user login module stores the registration information of the current user in the background database through the storage module; the front-end monitoring module monitors the user demands initiated by the user who logs in through the browser; and sends the monitored user demands to the back-end user demand The user demand parsing module is used to parse the received user demand, extract the user demand, and match the corresponding user operation items based on the keywords in the user demand, and then map and link the corresponding operation web page based on the user operation item, so as to display the page of the operation web page to the current logged-in user through the browser; wherein, the user operation items are not limited to: data loading and preprocessing, model reasoning, diagnosis, result viewing, data retrieval and interaction; the background processing module corresponding to the user operation items of data loading and preprocessing is the data loading and preprocessing module, which sends the relevant prompt information of the loading data format to the current logged-in user through the browser; when the current-end monitoring module monitors the user loading the corresponding After the data file is received, the currently loaded data file is used as the source data of the hippocampus segmentation of the current user and sent to the data loading and preprocessing module; the data loading and preprocessing module preprocesses the received source data of the hippocampus segmentation according to the established data preprocessing format, so that the source data matches the input of the segmentation model preset in the reasoning module (such as the segmentation model of the nnU-Net network); the preprocessed source data is then stored in the specified table item of the storage module; the background processing module corresponding to the user operation item of model reasoning is the reasoning module, and the reasoning module is pre-set with the trained segmentation model. When the front-end monitoring module monitors that the current user operation item of the user is model reasoning, it reads the segmentation model from the storage module. Take the most recent loaded data file of the current user, and input it into the segmentation model to perform forward reasoning operations to obtain the hippocampus segmentation result; at the same time, store the hippocampus segmentation result in the specified table item of the storage module, wherein the output form of the hippocampus segmentation result includes two-dimensional and three-dimensional forms; and visually output the source data of the hippocampus segmentation and the hippocampus segmentation result to the user by calling the first display module for three-dimensional display, and display the hippocampus segmentation result in two dimensions of different sections; the background processing module corresponding to the user operation item of diagnosis is a diagnosis module, which is used for users (mainly medical personnel) to input patient information, and for the diagnosis doctor to enter information describing and diagnosing the hippocampus segmentation result;The backend processing module corresponding to the user operation item of result viewing is the second display module, which is used to display the source data and hippocampus segmentation results of hippocampus segmentation in three dimensions, and to display the hippocampus segmentation results in two dimensions with different sections; the backend processing module corresponding to the user operation item of data retrieval is the query module, which is used to read the content matching the retrieval requirements from the storage module based on the retrieval requirements monitored by the front-end monitoring module and realize the visual output display of the retrieval through the second display module; the backend processing module corresponding to the user operation item of interaction is the interaction module, which is used to provide users with optional display content and form display selection operations for the result viewing content. ;

[0040] Figure 1 The overall technical route of the MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning in the embodiment of the present application is as follows: Figure 1 As shown: First, data is acquired, and then the generalization ability of the model is improved through data enhancement techniques such as rotation, translation, Gaussian transformation and brightness adjustment. Then, the nnU-Net network is used for model training and verification. The software platform is built on the Django framework, and 3D visualization is implemented through vtk.js, so that MRI data can be loaded in the interface, the optimal model is called for hippocampal segmentation, and 2D and 3D segmentation results are displayed. In addition, the platform also combines the MySQL database to realize the storage, management and historical record viewing of segmentation results. Finally, the project packaging is completed and the software is deployed.

[0041] In order to improve the generalization and robustness of the model, while enhancing the diversity of data and simulating the changing conditions of the real world, this embodiment adopts the following data enhancement strategy to reduce the risk of overfitting, optimize the accuracy of segmentation edges, and improve the efficiency of the training process:

[0042] (1) Rotation and translation: Rotation and translation are applied together to increase the computation speed. This application reduces the number of data interpolations required to one. Rotation and translation are each applied with a probability of 0.2. The rotation angles (in degrees) x, y and z are drawn from U(-30, 30) respectively. Translation is achieved by multiplying the coordinates in the voxel grid by a scaling factor. Therefore, a scaling factor less than 1 results in a "zooming out" effect, while values ​​greater than 1 result in a "zooming in" effect. The scaling factors for all types of blocks are drawn from U(0.7, 1.4).

[0043] (2) Gaussian noise: Gaussian noise centered at zero is added independently to each voxel in the sample with a probability of 0.15. Due to intensity normalization, the voxel intensities of all samples are close to zero mean and unit variance.

[0044] (3) Gaussian blur: blur is applied with probability 0.2 per sample. If this enhancement is triggered in a sample, blur is applied with probability 0.5 in the relevant modality (for samples with a single modality, there is only a joint probability of 0.1). The width of the Gaussian kernel (in voxels) σ is drawn from U(0.5, 1.5) independently for each modality.

[0045] (4) Brightness adjustment: With a probability of 0.15, the voxel intensity is multiplied by x~U(0.7,1.3).

[0046] (5) Contrast enhancement: With a probability of 0.15, the voxel intensity is multiplied by x~U(0.65,1.5). After the multiplication, the values ​​are truncated back to their original value range.

[0047] (6) Simulated low resolution: This augmentation is applied with probability 0.25 per sample and 0.5 per relevant mode. The triggered modes are downsampled by a factor of U(1,2) via nearest neighbor interpolation and then resampled back to their original size using cubic interpolation. For 2D blocks or anisotropic 3D blocks, this augmentation is applied only in 2D, preserving the out-of-plane axes (if applicable) in their original state.

[0048] (7) Gamma enhancement: This enhancement is applied with a probability of 0.15. The patch intensities are scaled to a factor of [0,1] of their respective value range. Then, a nonlinear intensity transformation is applied to each voxel:

[0049]

[0050] where γ~U(0.7,1.5) and i represents a voxel value. Subsequently, the voxel intensities are rescaled back to their original value range. With a probability of 0.15, this augmentation negates the voxel intensities before conversion:

[0051] 1-i new =(ii old ) γ (2)

[0052] The segmentation model selected for this platform is nnU-Net, which is a deep learning model customized for medical image segmentation. It integrates the advanced knowledge in the current field and can automatically adapt to different data sets and segmentation tasks. This model has set new records in 49 tasks in 19 international competitions, surpassing many other professional segmentation models.

[0053] In order to balance the evaluation and select the optimal model on different data subsets, this embodiment implements 5-fold cross validation. The specific method is to randomly divide the training set into five equal parts, each of which is used as a validation set in turn, and the rest are used as training sets. After the model is trained on the training set, it is evaluated on the validation set based on indicators such as Dice coefficient, Jaccard coefficient, PPV and HD95. This process is repeated five times, and the validation set is changed each time. Finally, the best weight configuration is selected based on the average performance and stability of the model on all validation sets.

[0054] The embodiment of the present application is based on the weight configuration trained by nnU-Net, and adopts a front-end and back-end separation architecture to develop a software platform. The front-end and back-end development process is as follows: Figure 2 As shown:

[0055] Front-end implementation: This platform is designed to be integrated with the trained nnU-Net model to achieve the goal of hippocampal segmentation. To this end, a navigation bar is designed at the top of the user interface of this platform, providing the following functions:

[0056] Home page: Provides an overview of the platform and quick access to key features. Users can upload brain MRI data and reconstruct and display it in 3D view. Click Start Segmentation to call the optimal model in the backend to automatically segment the hippocampus and intuitively display the results in 3D and 2D slices.

[0057] Result Viewing: allows users to review and analyze historical segmentation results, facilitating comparison and in-depth analysis. The data interaction between the home page and the result viewing page is as follows: Figure 3 shown.

[0058] Indicator Verification: Users can view indicators such as the Dice coefficient and Jaccard coefficient as well as the model training log to verify and evaluate the performance of the segmentation model. The data interaction process on the indicator verification page is as follows: Figure 4 shown.

[0059] Hippocampus Knowledge: Provides educational information about the hippocampus to enhance users’ understanding of its structure and function, and help users better prevent hippocampus-related diseases.

[0060] Backend implementation: In the backend development process of the platform, this embodiment uses the Django framework, which adopts the model-view-template (MVT) architecture mode to facilitate rapid development. At the same time, MySQL is also used, which is an efficient and stable open source relational database management system. The core functions of the backend include:

[0061] Automated data processing: Automated processing of MRI image data for accurate segmentation of the hippocampus.

[0062] Image visualization: Convert the segmentation results into 2D images for intuitive display in the front-end interface.

[0063] Data persistence: Ensure that the segmentation results are effectively stored in the MySQL database to facilitate subsequent queries.

[0064] Data retrieval: Provides the function of retrieving data from the database so that the front end can display the stored segmented data.

[0065] Performance evaluation: Calculate key segmentation metrics to display model performance in the front-end interface.

[0066] The implementation of these comprehensive functions provides powerful data interaction support for the front end, ensuring the smooth operation of the entire software platform and excellent user interaction experience.

[0067] In the embodiment of the present application, when the NIfTI 3D segmentation data of the hippocampus is visualized in 2D, it is a challenge to accurately locate the position of the hippocampus, because improper section selection may cause the hippocampus to be missing in the 2D view. In order to overcome this problem, the embodiment of the present application proposes a new 2D visualization method, which is based on finding the section with the largest 2D hippocampus area. By determining this optimal section and appropriately expanding it around it, it is possible to ensure that the hippocampus is included in the 2D visualization image, thereby improving the accuracy and reliability of visualization.

[0068] See also Figure 5 The 2D visualization processing flow of the embodiment of the present application specifically includes:

[0069] At the beginning of the process, first use the load_nii_file function (file loading function) to import the NIfTI file;

[0070] Then, verify that the loaded image and data are valid.

[0071] Next, the pixels and the largest slice are identified in the given three-dimensional array of the hippocampus through the find_max_slice function (identification function).

[0072] Then, use the plot function to plot the slice and its three adjacent slices, for a total of four images.

[0073] Finally, save these drawn images to files, and the 2D visualization process is completed.

[0074] In the embodiment of the present application, the specific processing process of the above find_max_slice function is:

[0075] Assume that data is a three-dimensional array, where data[k,i,j] represents the pixel value of the kth layer (slice), the ith row, and the jth column. The goal of the function is to find the slice index k that maximizes the following sum:

[0076]

[0077] Where M and N are the number of rows and columns of the slice, respectively.

[0078] To increase the speed of the algorithm, in this particular function, only slices of specified consecutive layers may be considered. Based on experience, this embodiment only considers slices from the 100th layer to the 129th layer, so the formula becomes:

[0079]

[0080] Finally, the function returns the k value that maximizes the above sum, which is max_slice.

[0081] In the embodiment of the present application, the specific execution process of the find_max_slice function is as follows: Figure 6 As shown, including:

[0082] Let A = data[k,i,j], then A is a three-dimensional array, and the execution goal is to find the index of the slice with the largest value in this matrix;

[0083] First, without changing the Y and Z coordinates, we search for the maximum value along the X axis using find_max_slice, and then find the maximum value and its index in each slice. This step does not require rotation, since we are only working in the original coordinate system.

[0084] Next, rotate the entire 3D matrix 90° around the Z axis. Mathematically, this can be achieved by applying a rotation matrix. The rotation matrix Rz(90°) for a point (x, y, z) in 3D space rotated 90° around the Z axis is:

[0085]

[0086] A ` =A*R z (90°) (6)

[0087] After applying this rotation matrix, we can get a new three-dimensional matrix A`. In the rotated matrix A`, find_max_slice is used to find the maximum value along the X axis again.

[0088] Then, rotate the matrix A' 90° around the Y axis. The rotation matrix Ry(90°) for a 90° rotation around the Y axis is:

[0089]

[0090] ``

[0091] A = A`*R z (90°) (8)

[0092] After applying this rotation matrix, another new three-dimensional matrix A`` can be obtained.

[0093] In the rotated matrix A``, we continue to search for the maximum value through find_max_slice. This time, we work in a coordinate system that has been rotated twice.

[0094] Finally, the maxima found in all rotated and unrotated coordinate systems are compared, and their indices are used for plotting, i.e., 2D visualization.

[0095] In order to evaluate the performance of the segmentation model used by the deep learning-based MRI hippocampus 3D intelligent analysis and diagnosis platform of this application, this embodiment uses the Dice coefficient, Jaccard coefficient, Positive Predictive Value (PPV) and HD95 (95% Hausdorff distance) as evaluation indicators. These indicators can fully reflect the accuracy, consistency and edge processing ability of the model in automatically segmenting the hippocampus area. These indicators will be explained one by one below:

[0096] (1) The Dice coefficient is a statistical tool used to evaluate the similarity between two samples. It can measure the consistency between automatic segmentation results and manual segmentation by experts and is a key indicator for evaluating the performance of segmentation algorithms. The Dice coefficient is defined as:

[0097]

[0098] Among them, A represents the hippocampus area segmented manually by experts, and B represents the hippocampus area segmented automatically.

[0099] (2) Jaccard coefficient is widely used in the field of image segmentation and can measure the similarity between the segmentation result and the true annotation. The definition of Jaccard coefficient is:

[0100]

[0101] (3) Positive Predictive Value (PPV) is also an indicator to measure segmentation performance. It calculates the ratio of the number of pixels correctly predicted as positive to all pixels predicted as positive. PPV is defined as:

[0102]

[0103] Among them, TP is the number of true positives, FP is the number of false positives, and the higher the PPV, the fewer misjudgments there are in the hippocampal area pixels predicted by the model, that is, the model's prediction is more accurate.

[0104] (4) HD95 (95% Hausdorff distance) is an indicator for evaluating the quality of image segmentation. It measures the maximum distance between the predicted segmentation result and the true annotation edge. The farthest 5% of points are ignored during calculation to reduce the impact of extreme values ​​and more accurately reflect the accuracy of the segmentation result. The smaller the HD95, the closer the boundary between the predicted segmentation and the true segmentation, that is, the stronger the model's ability in edge processing.

[0105] Table 1 Table 3.1 lists the performance of the nnU-net 3D model in each validation set in five cross-validations. Each validation independently tests the model to ensure the reliability of the evaluation. In terms of the three key indicators of Dice coefficient, Jaccard coefficient and PPV, the fourth fold validation results are the best: the average Dice coefficient is 0.9554, showing the high recognition accuracy of the model; the Jaccard coefficient is 0.9146, which proves its efficiency in regional overlap recognition; the PPV value of 0.9736 reflects the high true positive prediction rate of the model; the model also performs well in edge accuracy, controlling HD95 to about 1mm, which is crucial for segmenting fine hippocampal structures.

[0106] Table 1

[0107]

[0108] Figure 7 The comparison of the Dice coefficient, Jaccard coefficient, and PPV of the nnU-Net 3D model before and after data enhancement is shown. The results show that after data enhancement, each coefficient has been significantly improved. Figure 8 An example of homepage segmentation of the platform proposed in the embodiment of the present application is shown. Fig. 9 This is the result viewing page interface of this platform. Fig.10 and Fig.11 The following are the indicator verification interface and hippocampus knowledge popularization interface of this platform. On the homepage of this platform, users can explore the segmentation results in multiple dimensions through 3D and 2D views. In addition, a history display function is designed on the result viewing page, which enables doctors to easily review and compare past analysis results, thereby improving the accuracy and efficiency of diagnosis.

[0109] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0110] In addition, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include at least one of the features.

[0111] Any process or method description described in this specification may be understood to represent a module, fragment or portion of code including one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0112] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0113] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0115] The above are only some embodiments of the present application. For those skilled in the art, several modifications and improvements can be made without departing from the creative concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. MRI hippocampus 3D intelligent analysis and diagnosis platform based on deep learning, characterized by: include: User login module, front-end monitoring module, user demand analysis module, data loading and preprocessing module, reasoning module, first display module, second display module, diagnosis module, storage module, query module and interaction module; Among them, the user login module is used to implement browser-based user registration / and user login operations; if the current user is registering, the user login module stores the current user's registration information in the background database through the storage module; The front-end monitoring module monitors the user needs initiated by the user who logs in through the browser; and sends the monitored user needs to the back-end user demand parsing module; The user demand analysis module is used to analyze the received user demand, extract the user demand, and match the corresponding user operation item based on the keywords in the user demand, and then map and link the corresponding operation web page based on the user operation item, so as to display the operation web page to the currently logged-in user through the browser; Among them, user operation items include: data loading and preprocessing, model reasoning, diagnosis, result viewing, data retrieval and interaction; The background processing module corresponding to the user operation item of data loading and preprocessing is the data loading and preprocessing module, which sends relevant prompt information of the loading data format to the currently logged-in user through the browser; when the front-end monitoring module monitors the user loading the corresponding data file, the currently loaded data file is used as the source data of the hippocampus segmentation of the current user and sent to the data loading and preprocessing module; The data loading and preprocessing module preprocesses the received source data of hippocampal segmentation according to the established data preprocessing format so that the source data matches the input of the segmentation model preset in the inference module; and then stores the preprocessed source data in the specified table item of the storage module; The background processing module corresponding to the user operation item of model inference is the inference module, and a trained segmentation model is pre-installed in the inference module. When the front-end monitoring module monitors that the user's current user operation item is model inference, the most recent loaded data file of the current user is read from the storage module, and input into the segmentation model to perform forward reasoning operation to obtain the hippocampus segmentation result; at the same time, the hippocampus segmentation result is stored in the specified table item of the storage module, wherein the output form of the hippocampus segmentation result includes two-dimensional and three-dimensional forms; and by calling the first display module, the source data of the hippocampus segmentation and the hippocampus segmentation result are visually output to the user for three-dimensional display, and the hippocampus segmentation result is displayed in two dimensions with different sections; The background processing module corresponding to the user operation item of diagnosis is a diagnosis module, which is used for the user to input patient information and for the diagnosis doctor to input information describing and diagnosing the hippocampus segmentation results; The background processing module corresponding to the user operation item of result viewing is a second display module, which is used to display the source data of hippocampal segmentation and the hippocampal segmentation results in three dimensions, and to display the hippocampal segmentation results in two dimensions with different sections; The backend processing module corresponding to the user operation item of data retrieval is a query module, which is used to read the content matching the retrieval demand from the storage module based on the retrieval demand monitored by the front-end monitoring module and realize the visual output display of the retrieval through the second display module; The background processing module corresponding to the interactive user operation item is an interactive module, which is used to provide the user with a display selection operation of optional display content and form of the result viewing content.

2. The intelligent analysis and diagnosis platform according to claim 1, characterized in that: The display selection operations provided by the interactive module include: rotation, translation and / or color change.

3. The intelligent analysis and diagnosis platform according to claim 1, characterized in that: The query module visualizes the search content output by the second display module, including: hippocampus segmentation results, hippocampus volume, 2D maximum area, patient information, etc.

4. The intelligent analysis and diagnosis platform according to claim 1, characterized in that: The interactive functions provided by the interactive module also include: mouse hover zoom and page turning functions.

5. The intelligent analysis and diagnosis platform according to claim 1, characterized in that: The preset segmentation model in model inference is a segmentation model based on the nnU-Net model.

6. The intelligent analysis and diagnosis platform according to claim 1, characterized in that: Both the first display module and the second display module realize three-dimensional display through the 3D data visualization library tool vtk.js.

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