Cervical spinal canal stenosis diagnosis method and system based on deep learning

Abstract

The invention provides a cervical spinal canal stenosis diagnosis method, system and device based on deep learning and a computer readable storage medium. The cervical spinal canal stenosis diagnosis method based on deep learning comprises the following steps: acquiring a cervical vertebra X-ray image of a patient; inputting the cervical vertebra X-ray image into a preset cervical canal stenosis diagnosis model, and outputting a cervical canal stenosis diagnosis result; wherein the label of the cervical vertebra X-ray image in the training data set of the cervical vertebra canalis stenosis diagnosis model is determined according to the cervical vertebra canalis stenosis diagnosis result corresponding to the cervical vertebra MR image. According to the embodiment of the invention, the cervical spinal canal stenosis can be more accurately diagnosed based on the X-ray image.

Description

Technical Field

[0001] The present application relates to the field of cervical spinal stenosis, and in particular to a deep learning-based diagnosis method, system, device and computer-readable storage medium for cervical spinal stenosis. Background Art

[0002] Cervical spinal stenosis is a common cervical disease, which refers to the decrease in the diameter of the cervical spinal canal, resulting in compression of the spinal cord and nerve roots in the spinal canal, which may cause a series of clinical symptoms. This stenosis can be congenital or acquired, such as due to cervical degenerative lesions, bone hyperplasia, ossification of the yellow ligament, herniated disc, etc.

[0003] Symptoms of cervical spinal stenosis may include dizziness, headache, neck pain, arm pain, and numbness and weakness in the upper limbs. In severe cases, it may lead to weakness in the limbs and urinary and bowel disorders.

[0004] Diagnosis of cervical spinal stenosis usually requires a combination of the patient's symptoms, signs, and imaging examinations. MR images have unique advantages in diagnosing cervical spinal stenosis and spinal cord compression, and can clearly determine whether there are spinal cord signal changes and nerve compression.

[0005] X-ray images have advantages in cost-effectiveness, availability and simplicity, but doctors cannot diagnose cervical spinal stenosis with the naked eye based on X-ray images.

[0006] Therefore, how to more accurately diagnose cervical spinal stenosis based on X-ray images is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0007] The embodiments of the present application provide a method, system, device and computer-readable storage medium for diagnosing cervical spinal stenosis based on deep learning, which can more accurately diagnose cervical spinal stenosis based on X-ray images.

[0008] In a first aspect, an embodiment of the present application provides a method for diagnosing cervical spinal stenosis based on deep learning, comprising:

[0009] Obtain an X-ray image of the patient's cervical spine;

[0010] Input the cervical spine X-ray image into the preset cervical spinal canal stenosis diagnosis model, and output the cervical spinal canal stenosis diagnosis result;

[0011] Among them, the labels of cervical spine X-ray images in the training data set of the cervical spinal stenosis diagnosis model are determined according to the diagnosis results of cervical spinal stenosis of the corresponding cervical spine MR images.

[0012] Optional, the network structure of the cervical spinal stenosis diagnosis model includes:

[0013] Feature extraction, disparity attention extraction and feature reconstruction;

[0014] Among them, the feature extraction module stage uses a parameter-sharing modified binary feature fusion group to fuse different levels of features in a single view extracted by the multi-attention mechanism;

[0015] The disparity attention extraction stage introduces a dual-channel disparity attention mechanism and a pyramid sampling mechanism to fuse the local and global information between the two views;

[0016] The feature reconstruction stage continues the feature fusion group of the feature extraction module and reconstructs the high-resolution left view image through the automatic calculation parameter unit.

[0017] Optionally, the feature fusion group is built with a multi-attention fusion module as a basic module and a modified binarization fusion framework as a feature fusion framework.

[0018] Optional, including:

[0019] The main branch of the multi-attention fusion module first performs spatial feature extraction. The input features are extracted by stacking 3×3 convolutional layers, and the 1×1 convolution reduces the number of channels to 1 / 4 of the original.

[0020] In order to increase the receptive field, a convolution kernel with a stride of 2 and a 7×7 kernel is used to reduce the spatial dimension. Then, after the pooling layer, a dilation convolution operation with a dilation rate of 1 and 2 is performed respectively;

[0021] After upsampling to the original input feature dimension, the spatial feature tensors of the left view image and the right view image are output through a 1×1 convolution kernel sigmoid normalization operation.

[0022] Optionally, after obtaining an X-ray of the patient's cervical spine, include:

[0023] Perform image preprocessing on cervical spine X-ray images;

[0024] Among them, image preprocessing includes: filtering denoising, contrast enhancement and image normalization.

[0025] Optionally, filter denoising, contrast enhancement and image normalization are performed on the cervical spine X-ray image, including:

[0026] Use a median filter or a Gaussian filter to filter and denoise the cervical spine X-ray image;

[0027] For the filtered and denoised cervical spine X-ray images, histogram equalization is used to adjust the contrast and brightness of the images to enhance the visibility of the lesion areas in the images.

[0028] The contrast-enhanced cervical spine X-ray image is normalized so that the pixel values ​​are distributed within a preset range.

[0029] Optionally, during the model training process of the cervical spinal stenosis diagnosis model, set the training batch_size to 32;

[0030] Set the initial learning rate to 1e-4, and add a learning rate decay strategy. Every 5000 iterations, the learning rate decays to 0.9 of the previous learning rate.

[0031] Set the optimizer to Adam optimizer;

[0032] Set the loss function to DICEloss;

[0033] The training set and validation set were validated once for every 1000 iterations. The early stopping method was used to determine the stopping time of network training, and a diagnostic model for cervical spinal stenosis was obtained.

[0034] In a second aspect, the embodiment of the present application provides a deep learning-based diagnosis system for cervical spinal stenosis, comprising:

[0035] A cervical spine X-ray image acquisition module, used for acquiring a patient's cervical spine X-ray image;

[0036] The cervical spinal canal stenosis diagnosis module is used to input the cervical X-ray image into the preset cervical spinal canal stenosis diagnosis model and output the cervical spinal canal stenosis diagnosis result;

[0037] Among them, the labels of cervical spine X-ray images in the training data set of the cervical spinal stenosis diagnosis model are determined according to the diagnosis results of cervical spinal stenosis of the corresponding cervical spine MR images.

[0038] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: a processor and a memory storing computer program instructions;

[0039] When the processor executes the computer program instructions, a deep learning-based method for diagnosing cervical spinal stenosis is implemented.

[0040] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement a deep learning-based method for diagnosing cervical spinal stenosis.

[0041] The deep learning-based diagnostic method, system, device and computer-readable storage medium for cervical spinal stenosis of the embodiments of the present application can more accurately diagnose cervical spinal stenosis based on X-ray images.

[0042] The deep learning-based diagnostic method for cervical spinal stenosis includes:

[0043] Obtain an X-ray image of the patient's cervical spine;

[0044] Input the cervical spine X-ray image into the preset cervical spinal canal stenosis diagnosis model, and output the cervical spinal canal stenosis diagnosis result;

[0045] Among them, the labels of cervical spine X-ray images in the training data set of the cervical spinal stenosis diagnosis model are determined according to the diagnosis results of cervical spinal stenosis of the corresponding cervical spine MR images. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a flowchart of a method for diagnosing cervical spinal stenosis based on deep learning provided by an embodiment of the present application;

[0048] Figure 2 is a schematic diagram of the network structure of a cervical spinal stenosis diagnosis model provided by an embodiment of the present application;

[0049] Figure 3 is a schematic diagram of a network structure of a feature fusion group provided by an embodiment of the present application;

[0050] Figure 4 is a schematic diagram of the network structure of a multi-attention fusion module provided by an embodiment of the present application;

[0051] Figure 5 It is a schematic diagram of the structure of a deep learning-based cervical spinal stenosis diagnosis system provided by an embodiment of the present application;

[0052] Figure 6 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0054] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0055] In order to solve the problems of the prior art, the embodiments of the present application provide a method, system, device and computer-readable storage medium for diagnosing cervical spinal stenosis based on deep learning. The following first introduces the method for diagnosing cervical spinal stenosis based on deep learning provided by the embodiments of the present application.

[0056] Figure 1 FIG. 1 is a flow chart of a method for diagnosing cervical spinal stenosis based on deep learning provided by an embodiment of the present application. Figure 1 As shown, the deep learning-based diagnostic method for cervical spinal stenosis includes:

[0057] S101, obtaining a cervical spine X-ray image of the patient;

[0058] S102, inputting the cervical spine X-ray image into a preset cervical spinal stenosis diagnostic model, and outputting the cervical spinal stenosis diagnostic result; wherein, the label of the cervical spine X-ray image in the training data set of the cervical spinal stenosis diagnostic model is determined according to the cervical spinal stenosis diagnostic result of the corresponding cervical spine MR image.

[0059] Figure 2 is a schematic diagram of the network structure of a cervical spinal stenosis diagnosis model provided by an embodiment of the present application;

[0060] In one embodiment, the network structure of the cervical spinal stenosis diagnosis model includes:

[0061] Feature extraction, disparity attention extraction and feature reconstruction;

[0062] Among them, the feature extraction module stage uses a parameter-sharing modified binary feature fusion group to fuse different levels of features in a single view extracted by the multi-attention mechanism;

[0063] The disparity attention extraction stage introduces a dual-channel disparity attention mechanism and a pyramid sampling mechanism to fuse the local and global information between the two views;

[0064] The feature reconstruction stage continues the feature fusion group of the feature extraction module and reconstructs the high-resolution left view image through the automatic calculation parameter unit.

[0065] Figure 3 is a schematic diagram of a network structure of a feature fusion group provided by an embodiment of the present application;

[0066] In one embodiment, the feature fusion group is built with a multi-attention fusion module as a basic module and a modified binary fusion framework as a feature fusion framework.

[0067] Figure 4 is a schematic diagram of the network structure of a multi-attention fusion module provided by an embodiment of the present application;

[0068] In one embodiment, it includes:

[0069] The main branch of the multi-attention fusion module first performs spatial feature extraction. The input features are extracted by stacking 3×3 convolutional layers, and the 1×1 convolution reduces the number of channels to 1 / 4 of the original.

[0070] In order to increase the receptive field, a convolution kernel with a stride of 2 and a 7×7 kernel is used to reduce the spatial dimension. Then, after the pooling layer, a dilation convolution operation with a dilation rate of 1 and 2 is performed respectively;

[0071] After upsampling to the original input feature dimension, the spatial feature tensors of the left view image and the right view image are output through a 1×1 convolution kernel sigmoid normalization operation.

[0072] In one embodiment, after obtaining a cervical spine X-ray image of the patient, the method includes:

[0073] Perform image preprocessing on cervical spine X-ray images;

[0074] Among them, image preprocessing includes: filtering denoising, contrast enhancement and image normalization.

[0075] In one embodiment, filtering, denoising, contrast enhancement and image normalization are performed on a cervical spine X-ray image, including:

[0076] Use a median filter or a Gaussian filter to filter and denoise the cervical spine X-ray image;

[0077] For the filtered and denoised cervical spine X-ray images, histogram equalization is used to adjust the contrast and brightness of the images to enhance the visibility of the lesion areas in the images.

[0078] The contrast-enhanced cervical spine X-ray image is normalized so that the pixel values ​​are distributed within a preset range.

[0079] In one embodiment, during the model training process of the cervical spinal stenosis diagnosis model, the batch_size of the training is set to 32;

[0080] Set the initial learning rate to 1e-4, and add a learning rate decay strategy. Every 5000 iterations, the learning rate decays to 0.9 of the previous learning rate.

[0081] Set the optimizer to Adam optimizer;

[0082] Set the loss function to DICEloss;

[0083] The training set and validation set were validated once for every 1000 iterations. The early stopping method was used to determine the stopping time of network training, and a diagnostic model for cervical spinal stenosis was obtained.

[0084] Figure 5 It is a schematic diagram of the structure of a deep learning-based cervical spinal stenosis diagnosis system provided by an embodiment of the present application;

[0085] The deep learning-based cervical spinal stenosis diagnostic system includes:

[0086] A cervical spine X-ray image acquisition module 501 is used to acquire a cervical spine X-ray image of a patient;

[0087] The cervical spinal canal stenosis diagnosis module 502 is used to input the cervical X-ray image into a preset cervical spinal canal stenosis diagnosis model and output the cervical spinal canal stenosis diagnosis result;

[0088] Among them, the labels of cervical spine X-ray images in the training data set of the cervical spinal stenosis diagnosis model are determined according to the diagnosis results of cervical spinal stenosis of the corresponding cervical spine MR images.

[0089] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown.

[0090] The electronic device may include a processor 601 and a memory 602 storing computer program instructions.

[0091] Specifically, the processor 601 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0092] The memory 602 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 602 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 602 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 602 may be inside or outside the electronic device. In a particular embodiment, the memory 602 may be a non-volatile solid-state memory.

[0093] In one embodiment, the memory 602 may be a read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0094] The processor 601 reads and executes computer program instructions stored in the memory 602 to implement any one of the deep learning-based methods for diagnosing cervical spinal stenosis in the above-mentioned embodiments.

[0095] In one example, the electronic device may further include a communication interface 603 and a bus 610. Figure 6 As shown, the processor 601, the memory 602, and the communication interface 603 are connected via a bus 610 and communicate with each other.

[0096] The communication interface 603 is mainly used to implement communication between various modules, systems, units and / or devices in the embodiments of the present application.

[0097] Bus 610 includes hardware, software or both, and the parts of electronic equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 610 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0098] In addition, in combination with the deep learning-based diagnostic method for cervical spinal canal stenosis in the above-mentioned embodiments, the embodiments of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the deep learning-based diagnostic methods for cervical spinal canal stenosis in the above-mentioned embodiments is implemented.

[0099] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0100] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0101] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or systems. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0102] The above reference is according to the method of the embodiment of the present application, the flowchart and / or block diagram of the system and computer program product described various aspects of the present application.It should be understood that each square block in the flowchart and / or block diagram and the combination of each square block in the flowchart and / or block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing system, to produce a kind of machine, so that these instructions executed by the processor of the computer or other programmable data processing system enable the realization of the function / action specified in one or more square blocks of the flowchart and / or block diagram.Such a processor can be but is not limited to a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.It can also be understood that each square block in the block diagram and / or the flowchart and the combination of the square blocks in the block diagram and / or the flowchart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0103] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for diagnosing cervical spinal stenosis based on deep learning, characterized in that: include: Obtain an X-ray image of the patient's cervical spine; Input the cervical spine X-ray image into the preset cervical spinal canal stenosis diagnosis model, and output the cervical spinal canal stenosis diagnosis result; Among them, the labels of cervical spine X-ray images in the training data set of the cervical spinal stenosis diagnosis model are determined according to the diagnosis results of cervical spinal stenosis of the corresponding cervical spine MR images.

2. The method for diagnosing cervical spinal stenosis based on deep learning according to claim 1, characterized in that: The network structure of the cervical spinal stenosis diagnosis model includes: Feature extraction, disparity attention extraction and feature reconstruction; Among them, the feature extraction module stage uses a parameter-sharing modified binary feature fusion group to fuse different levels of features in a single view extracted by the multi-attention mechanism; The disparity attention extraction stage introduces a dual-channel disparity attention mechanism and a pyramid sampling mechanism to fuse the local and global information between the two views; The feature reconstruction stage continues the feature fusion group of the feature extraction module and reconstructs the high-resolution left view image through the automatic calculation parameter unit.

3. The method for diagnosing cervical spinal stenosis based on deep learning according to claim 2, characterized in that: The feature fusion group is built with the multi-attention fusion module as the basic module and the modified binary fusion framework as the feature fusion framework.

4. The method for diagnosing cervical spinal stenosis based on deep learning according to claim 3, characterized in that: include: The main branch of the multi-attention fusion module first performs spatial feature extraction. The input features are extracted by stacking 3×3 convolutional layers, and the 1×1 convolution reduces the number of channels to 1 / 4 of the original. In order to increase the receptive field, a convolution kernel with a stride of 2 and a 7×7 kernel is used to reduce the spatial dimension. Then, after the pooling layer, a dilation convolution operation with a dilation rate of 1 and 2 is performed respectively; After upsampling to the original input feature dimension, the spatial feature tensors of the left view image and the right view image are output through a 1×1 convolution kernel sigmoid normalization operation.

5. The method for diagnosing cervical spinal stenosis based on deep learning according to claim 1, characterized in that: After obtaining an X-ray of the patient's cervical spine, include: Perform image preprocessing on cervical spine X-ray images; Among them, image preprocessing includes: filtering denoising, contrast enhancement and image normalization.

6. The method for diagnosing cervical spinal stenosis based on deep learning according to claim 5, characterized in that: Perform filtering, denoising, contrast enhancement and image normalization on cervical spine X-ray images, including: Use a median filter or a Gaussian filter to filter and denoise the cervical spine X-ray image; For the filtered and denoised cervical spine X-ray images, histogram equalization is used to adjust the contrast and brightness of the images to enhance the visibility of the lesion areas in the images. The contrast-enhanced cervical spine X-ray image is normalized so that the pixel values ​​are distributed within a preset range.

7. The method for diagnosing cervical spinal stenosis based on deep learning according to claim 1, characterized in that: During the model training process of the cervical spinal stenosis diagnosis model, the batch_size of the training is set to 32; Set the initial learning rate to 1e-4, and add a learning rate decay strategy. Every 5000 iterations, the learning rate decays to 0.9 of the previous learning rate. Set the optimizer to Adam optimizer; Set the loss function to DICEloss; The training set and validation set were validated once for every 1000 iterations. The early stopping method was used to determine the stopping time of network training, and a diagnostic model for cervical spinal stenosis was obtained.

8. A deep learning-based diagnostic system for cervical spinal stenosis, characterized in that: The system comprises: A cervical spine X-ray image acquisition module, used for acquiring a patient's cervical spine X-ray image; The cervical spinal canal stenosis diagnosis module is used to input the cervical X-ray image into the preset cervical spinal canal stenosis diagnosis model and output the cervical spinal canal stenosis diagnosis result; Among them, the labels of cervical spine X-ray images in the training data set of the cervical spinal stenosis diagnosis model are determined according to the diagnosis results of cervical spinal stenosis of the corresponding cervical spine MR images.

9. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the deep learning-based method for diagnosing cervical spinal stenosis as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the deep learning-based method for diagnosing cervical spinal stenosis as described in any one of claims 1 to 7.

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