CN111383222A - Intervertebral disc MRI image intelligent diagnosis system based on deep learning - Google Patents
Intervertebral disc MRI image intelligent diagnosis system based on deep learning Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 34
- 238000013135 deep learning Methods 0.000 title claims abstract description 13
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- 238000007781 pre-processing Methods 0.000 claims abstract description 13
- 238000000034 method Methods 0.000 claims abstract description 12
- 238000013500 data storage Methods 0.000 claims abstract description 11
- 238000001514 detection method Methods 0.000 claims abstract description 11
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- 208000008035 Back Pain Diseases 0.000 description 2
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Abstract
The invention discloses an intervertebral disc MRI image intelligent diagnosis system based on deep learning. The method comprises the following steps: the data storage subsystem is used for storing data required by model training; the data processing subsystem is used for preprocessing data, expanding data, labeling data and converting data; a model training subsystem for training a network of modified YOLOv 3; and the real-time display subsystem is used for acquiring the MRI picture of the anterior vertebral disc to be detected uploaded by a user and displaying a diagnosis result. The invention adds an interconnection system structure on the basis of the original YOLOv3, improves the performance of multi-scale target detection by sharing the feature mapping information of different scales, and reduces the number of convolution layers in a detection module to reduce the number of network model parameters, thereby accelerating the training network and the network reasoning speed. The method can realize real-time, automatic and accurate detection and classification of the intervertebral disc MRI pictures, and assist doctors to make the next treatment scheme.
Description
Technical Field
The invention belongs to the technical field of deep learning, and particularly relates to an intervertebral disc MRI image intelligent diagnosis system based on deep learning.
Background
Degeneration of the intervertebral disc (IVD) is one of the major causes of chronic Low Back Pain (LBP), which has become a major public health problem in our society and a major cause of disability. Magnetic Resonance Imaging (MRI) is the first modality to assess degenerative disc disease in the lumbar spine, which provides good soft tissue contrast without the risk of ionizing radiation and is more acceptable to patients. In recent years, the research and application of multi-modality MRI has further improved the quality of diagnosis, treatment and follow-up of a variety of diseases, but these methods still have many problems:
(1) the visual inspection of the intervertebral disc image is time-consuming and labor-consuming, a doctor needs to accurately outline the outline of the intervertebral disc protrusion part and then adopts a corresponding treatment means according to the pathological change degree, however, the process is closely related to the personal experience and the service capability of the doctor, the risk of missed diagnosis or misdiagnosis exists, the process is complicated, the efficiency is low, and the accuracy is relatively low.
(2) Due to the different intensity distribution and deformation degree of the intervertebral disc images, the intervertebral disc images present a wider boundary range in different MRI scanners, and unnecessary data volume is increased, which brings burden to doctors for disease assessment.
In view of this, it is increasingly difficult for traditional research methods relying on the diagnosis of intervertebral disc MRI images by specialized physicians to meet the real-time and accuracy requirements. Therefore, it is a real need in the medical industry to find a reliable automatic diagnosis system for intervertebral disc images. The invention designs an intervertebral disc MRI image intelligent diagnosis system based on deep learning, which is characterized in that case intervertebral disc MRI image data are input and processed, the data comprise data preprocessing, data expansion, data labeling, data conversion and data classification, then the case intervertebral disc MRI image data are trained through an improved YOLOv3 network, and the trained models are stored. When the MRI picture of the intervertebral disc to be detected is transmitted, the pathological change position of the intervertebral disc can be accurately positioned in real time, and a doctor is assisted to formulate a next treatment scheme.
Disclosure of Invention
The invention aims to provide an intervertebral disc MRI image intelligent diagnosis system based on deep learning, which overcomes the defects of the traditional artificial diagnosis and assists doctors in carrying out classified diagnosis on intervertebral disc lesions.
The invention is realized by the following steps: an intervertebral disc MRI image intelligent diagnosis system based on deep learning comprises: the system comprises a data storage subsystem, a data processing subsystem, a model training subsystem and a real-time display subsystem; the data processing subsystem includes: the data conversion module is connected with the data marking module; the real-time display subsystem includes: RESTful API module and result display module, two modules link to each other in order. The subsystems and the modules are connected through a wired and/or WiFi wireless and/or 3G/4G/5G wireless network.
The storage subsystem stores data needed by model training, converted data, a trained model and an intervertebral disc MRI picture to be detected uploaded by a user.
The data processing subsystem comprises a data preprocessing module, a data expansion module, a data labeling module and a data conversion module.
The data preprocessing module is used for carrying out data cleaning, data integration, data reduction and data conversion on the intervertebral disc MRI data in the storage subsystem.
The data expansion module adopts methods including transformation, rotation, clipping, scaling and the like, and is characterized in that the original image is processed and processed by using the traditional mathematical form so as to obtain enough data to support the next training.
And the data labeling module is used for labeling the intervertebral disc area on the data set by using a common data labeling tool Labelme in a polygonal mode and marking a multi-classification label, and the labeled intervertebral disc area is stored as a json file.
And the data conversion module analyzes the json files after the label of the Labelme tool is generated into training available files in batch, wherein the training available files comprise intervertebral disc class files, training set label files, verification set label files and class index files.
The model training subsystem sends the processed intervertebral disc MRI samples to a neural network model with preset darknet53 pre-training weights provided by the darknet for training, improves the architecture of the YOLOv3, increases the interconnection architecture, shares feature mapping information of different scales, and improves the performance of multi-scale target detection.
The real-time display subsystem comprises a RESTful API module and a result display module.
The RESTful API module supports a user to send an HTTPS request to send a picture to be detected through an API provided by the intervertebral disc MRI image intelligent diagnosis system, the picture to be detected is classified in a trained model, meanwhile, the intervertebral disc MRI picture to be diagnosed submitted by the user is marked to form a new sample, the new sample is added into an original sample library, and data in the sample library is increased.
And after the diagnosis is finished, the result display module returns the diagnosis result to the browser or an application program calling the API.
Compared with the prior art, the intervertebral disc MRI image intelligent diagnosis system based on deep learning provided by the invention has the following beneficial effects:
1. the improved YOLOv3 network is trained through a large amount of intervertebral disc MRI picture data to obtain a stable network model, so that automatic high-precision intervertebral disc disease diagnosis is realized, and the burden of doctors on intervertebral disc disease diagnosis is reduced.
2. The improved YOLOv3 network increases an interconnection system structure, and improves the performance of multi-scale target detection by sharing the feature mapping information of different scales. Meanwhile, the number of the convolution layers in the candidate bounding box area is reduced, and the number of the network model parameters is correspondingly reduced, so that the training network and the network reasoning speed are accelerated.
3. By applying a series of data expansion methods, including the processes of intervertebral disc MRI picture transformation, rotation, cutting and scaling in the early stage, and adding the intervertebral disc MRI data to be detected submitted by a user to an original sample library, the data in the sample library is continuously increased, the problem that a more accurate network model is difficult to train due to less obtained intervertebral disc MRI data in the early stage is solved, and the stability of the network model and the accuracy of a diagnosis system are continuously improved.
Drawings
FIG. 1 is a schematic diagram of a system configuration according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a data expansion method of an intervertebral disc MRI picture based on an ROI (region of interest) area according to the invention;
fig. 3 is a network architecture diagram according to an embodiment of the present invention.
Detailed Description
Example (b):
as shown in fig. 1, an embodiment provides a deep learning based intervertebral disc MRI image intelligent diagnosis system, including: the system comprises a data storage subsystem 1, a data processing subsystem 6, a model training subsystem 7 and a real-time display subsystem 10; the data processing subsystem 6 comprises a data preprocessing module 2, a data expansion module 3, a data labeling module 4 and a data conversion module 5, wherein the data preprocessing module 2 is connected with the data expansion module 3, the data expansion module 3 is connected with the data labeling module 4, and the data labeling module 4 is connected with the data conversion module 5; the real-time display subsystem 10 comprises a RESTful API module 8 and a result display module 9, which are connected in sequence. The subsystems and the modules are connected through a wired and/or WiFi wireless and/or 3G/4G/5G wireless network.
The data storage subsystem 1 stores an intervertebral disc MRI picture data set, converted data, a trained model and an intervertebral disc MRI picture to be detected, which is subsequently uploaded by a user and is required to be used by model training, through a local disk database or a cloud disk database.
The data processing subsystem 6 comprises a data preprocessing module 2, a data expansion module 3, a data labeling module 4 and a data conversion module 5.
The data preprocessing module 2 performs data cleaning, data integration, data reduction and data conversion on data in the database, solves the problems of noise, inconsistency, numerical value loss and the like of original data, improves the overall quality of the data, and then stores the data subjected to a data preprocessing stage into the data storage subsystem 1.
The principle of the data expansion module 3 is as shown in fig. 2, and sample data is expanded through operations such as transformation, rotation, clipping, scaling and the like, so that the effective data expansion can expand the number of training samples and increase the diversity of the training samples; the trained model can avoid overfitting and improve the performance of the model.
The data labeling module 4 labels the pictures by using a Labelme tool, and adopts polygon labeling to replace default rectangular labeling, so as to increase the accuracy of later-stage identification and facilitate subsequent example segmentation. The annotation results are stored in the form of json files, and are divided into four categories, the first being normal disc (normal), the second being degeneration of the disc (degeneration), the third being Protrusion of the anterior disc (prolapse), and the fourth being herniated disc (prolapse).
The data conversion module 5 converts the json files generated in the previous step into corresponding data files in batch by performing data conversion on a terminal, wherein the data files comprise intervertebral disc class files, training set label files, verification set label files and class index files, and are stored in the data storage subsystem 1.
The model training subsystem 7 trains, verifies and evaluates a preset neural network model through a training set, a verification set and a test set, the data distribution ratio is 8: 1: 1, an improved YOLOv3 network architecture is shown in fig. 3, an interconnection architecture is added on the basis of an original YOLOv3, the performance of multi-scale target detection is improved by sharing feature mapping information of different scales, meanwhile, the number of convolutional layers in a detection module is reduced to reduce the number of network model parameters, thereby accelerating network training and network inference speed, a gray image with the size of 512 in the data set is input into a training network, a feature extraction layer in the network is responsible for extracting feature maps of three scales (128 ×, 64 ×, 32 2) in the image, the three feature maps are input into a right module for further processing, features under the three scales are extracted respectively, and finally, a semantic block3 and a semantic block 493 classification block are given by a boundary predictor and a semantic block3 for achieving a semantic classification of a semantic classification, a semantic classification rule, a regression learning rate of a Convolution learning rate of 512, a random learning rate, a convolutional learning rate, a Gradient.
The real-time display subsystem 10 comprises a RESTful API module 8 and a result display module 9.
The RESTful API module 8 supports a user to send an HTTPS request to send a picture to be detected through an API provided by the intervertebral disc MRI image intelligent diagnosis system, the picture to be detected is classified in the trained model, and is stored in the data storage subsystem 1 and added into the original intervertebral disc MRI picture sample library, so that data in the sample library is continuously increased, the trained neural network model is more and more stable, and the recognition rate is continuously increased.
And after the diagnosis is finished, the result display module 9 returns the diagnosis result to the browser or an application program calling the API, and if the pathological changes occur, the pathological change area is marked and an alarm is given, so that a doctor is assisted to make a next treatment scheme.
The intervertebral disc MRI image intelligent diagnosis system based on deep learning has the working process as follows:
(1) first, a disc MRI picture stored in the storage subsystem is acquired.
(2) And then preprocessing the picture data, including data cleaning, data integration, data reduction and data conversion, solving the problems of noise, inconsistency, numerical value loss and the like of the original data, and improving the overall quality of the data.
(3) And then, the data set subjected to data preprocessing is subjected to operations such as transformation, rotation, clipping, scaling and the like, and the number of samples is expanded.
(4) And labeling the picture by utilizing a Labelme tool, and replacing default rectangular labeling with polygonal labeling so as to hopefully increase the accuracy of later-stage identification and facilitate subsequent instance segmentation. The labeling results are stored in a json file form and are divided into four types, namely normal intervertebral disc, intervertebral disc degeneration, intervertebral disc protrusion and intervertebral disc prolapse.
(5) And analyzing the json file generated by the labeling in batches into training available files including an intervertebral disc class file, a training set label file, a verification set label file and a class index file, and storing the training available files after the analysis in batches into a data storage subsystem.
(6) And inputting the result processed samples in the data storage subsystem into a modified YOLOv3 network for training, wherein the distribution ratio of the training set, the verification set and the test set is 8: 1: 1, an interconnection architecture is added on the basis of the original YOLOv3, and the performance of multi-scale target detection is improved by sharing feature mapping information of different scales. Meanwhile, the number of convolution layers in the detection module is reduced so as to reduce the number of network model parameters, the training network and the network reasoning speed are accelerated, then the error of the network output result and the label data is calculated, an SGD optimization function is adopted to optimize the network in training until the threshold is met, if the threshold is met, the next step of verification and test is carried out, otherwise, the training is returned to continue, and the trained model is stored in the data storage subsystem.
(7) A user sends an HTTPS request to send a picture to be detected through an API provided by an intervertebral disc MRI image intelligent diagnosis system, the picture to be detected is classified in a trained model, the intervertebral disc MRI picture to be diagnosed submitted by the user is marked to form a new sample, the new sample is added into an original sample library, data in the sample library is continuously increased to deal with special cases which may appear when the model is actually applied, accordingly, the trained neural network model is more and more stable, the identification accuracy rate is continuously increased, and doctors are assisted to better make a next treatment scheme.
The intervertebral disc MRI image intelligent diagnosis system based on deep learning can realize real-time intervertebral disc disease diagnosis, reduce the burden of doctors on intervertebral disc disease diagnosis and improve the detection efficiency and accuracy.
While the foregoing is directed to the preferred embodiment of the present invention, it is understood that the foregoing is illustrative only and is not to be construed as limiting the scope of the invention, as numerous changes and modifications will become apparent to those skilled in the art in light of the foregoing description.
Claims (1)
1. An intervertebral disc MRI image intelligent diagnosis system based on deep learning is characterized in that: the system comprises a data storage subsystem, a data processing subsystem, a model training subsystem and a real-time display subsystem; the data processing subsystem includes: the data conversion module is connected with the data marking module; the real-time display subsystem includes: the RESTful API module and the result display module are sequentially connected, and the subsystems and the modules are connected through a wired and/or WiFi wireless and/or 3G/4G/5G wireless network;
the storage subsystem stores data needed by model training, converted data, a trained model and a to-be-detected intervertebral disc MRI picture uploaded by a user;
the data processing subsystem comprises a data preprocessing module, a data expansion module, a data labeling module and a data conversion module;
the data preprocessing module is used for carrying out data cleaning, data integration, data reduction and data conversion on the intervertebral disc MRI data in the storage subsystem;
the method adopted by the data expansion module comprises the steps of transformation, rotation, clipping and scaling treatment, and is characterized in that the original image is processed and processed by utilizing the traditional mathematical form so as to obtain enough data to support the next training;
the data marking module marks the intervertebral disc area on the data set in a polygon mode by using a common data marking tool Labelme, marks the intervertebral disc area on the data set with a multi-classification label, and saves the marked intervertebral disc area as a json file;
the data conversion module analyzes the json files after the label of the Labelme tool is generated into training available files in batch, wherein the training available files comprise intervertebral disc class files, training set label files, verification set label files and class index files;
the model training subsystem sends the processed intervertebral disc MRI samples into a neural network model with preset darknet53 pre-training weight provided by the darknet for training, improves the architecture of the YOLOv3, increases the interconnection architecture, shares the feature mapping information of different scales, and improves the performance of multi-scale target detection;
the real-time display subsystem comprises a RESTful API module and a result display module;
the RESTful API module supports a user to send an HTTPS request to send a picture to be detected through an API provided by the intervertebral disc MRI image intelligent diagnosis system, the picture to be detected is classified in a trained model, meanwhile, the intervertebral disc MRI picture to be diagnosed submitted by the user is marked to form a new sample, the new sample is added into an original sample library, and data in the sample library is increased;
and after the diagnosis is finished, the result display module returns the diagnosis result to the browser or an application program calling the API.
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