CN110874614A - Brain image classification method, computer device and readable storage medium - Google Patents
Brain image classification method, computer device and readable storage medium Download PDFInfo
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Abstract
The invention relates to a brain image classification method, a computer device and a readable storage medium, wherein the method comprises the following steps: acquiring a brain image to be classified; inputting the brain image into the spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image; and inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image. According to the method, the computer equipment can accurately map the features of the brain image to be classified into the cortical spherical mapping map through the spherical mapping model, so that the accuracy of the cortical spherical mapping map corresponding to the brain image to be classified is improved, and the obtained cortical spherical mapping map can accurately display the structural change of the cortex of the brain image to be classified, so that the spherical convolution network can more accurately extract the features of the cortex of the brain image to be classified, the brain image to be classified is more accurately classified, and the classification result of the obtained brain image to be classified is improved.
Description
Technical Field
The present invention relates to the field of medical images, and in particular, to a brain image classification method, a computer device, and a readable storage medium.
Background
Neurological and psychiatric disorders can cause changes in brain structure, and brain atrophy is exemplified as a phenomenon in which organic lesions occur in brain tissue due to various causes to cause atrophy, and the pathological manifestations are that the volume of brain tissue is reduced, the number of cells is reduced, and the ventricles of brain and subarachnoid space are enlarged. The atrophy of cerebral cortex, cerebellum, brain stem and the like is one of neuroimaging manifestations of many diseases such as Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Parkinson's Disease (PD) and the like, and Magnetic Resonance Imaging (MRI) can be used for diagnosing AD and related diseases due to the characteristics of no radioactivity, high Imaging quality of brain structures and the fact that the atrophy of cortex, cerebellum, brain stem and the like is visible.
In the traditional technology, a neural network based on deep convolution directly expands an MRI image into a planar image through conformal mapping, then performs convolution operation on the expanded planar image so as to obtain feature representations of the MRI image at different levels, classifies the corresponding MRI image according to the features of the MRI image at different levels, and performs auxiliary diagnosis on diseases.
However, the conventional method for classifying the MRI image has a problem that the classification result is inaccurate.
Disclosure of Invention
Based on this, it is necessary to provide a brain image classification method, a computer device and a readable storage medium, aiming at the problem that the conventional method for classifying MRI images has inaccurate classification results.
In a first aspect, an embodiment of the present invention provides a method for classifying brain images, where the method includes:
acquiring a brain image to be classified;
inputting the brain image into a spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image;
and inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image.
In one embodiment, the inputting the brain image into a spherical mapping model to obtain a cortical spherical mapping map corresponding to the brain image includes:
inputting the brain image into the spherical mapping model to obtain a cerebral cortex spherical expansion image corresponding to the brain image;
and mapping the cerebral cortex spherical expansion map to a preset standard spherical surface to obtain a cerebral cortex spherical mapping map corresponding to the cerebral image.
In one embodiment, the inputting the brain image into a spherical mapping model to obtain a cortical spherical expansion map corresponding to the brain image includes:
and inputting the brain image into the spherical mapping model, and unfolding the brain image according to a preset unfolding rule to obtain a cerebral cortex spherical unfolding image corresponding to the brain image.
In one embodiment, the network structure of the spherical convolutional network is any one of the following: a three-dimensional dense connection network structure; a spherical residual network structure; a three-dimensional dense connection network incorporates a feature weighting module network structure.
In one embodiment, the spherical mapping model comprises a spherical mapping algorithm or a spherical mapping network.
In one embodiment, when the spherical mapping model is a spherical mapping network, the training process of the spherical mapping model includes:
obtaining a sample brain image;
inputting the sample brain image into a preset spherical mapping network to obtain a sample cerebral cortex spherical mapping image corresponding to the sample brain image;
and training the preset spherical mapping network according to the sample cortical spherical mapping map and a preset reference cortical spherical mapping map to obtain the spherical mapping network.
In one embodiment, before the training the preset spherical mapping network according to the sample cortical spherical mapping map and a preset reference cortical spherical mapping map to obtain the spherical mapping network, the method further includes:
and processing the sample brain image by using a preset brain image segmentation tool to obtain the preset reference cerebral cortex spherical mapping image.
In one embodiment, the training process of the spherical convolutional network comprises:
obtaining a sample cerebral cortex spherical mapping map;
inputting the sample cerebral cortex spherical mapping map into a preset spherical convolution network to obtain a sample classification result of a sample cerebral image corresponding to the sample cerebral cortex spherical mapping map;
and training the preset spherical convolution network according to the sample classification result and a label which is labeled on the sample brain image corresponding to the sample cerebral cortex spherical mapping image in advance to obtain the spherical convolution network.
In a second aspect, an embodiment of the present invention provides a brain image classification apparatus, including:
the first acquisition module is used for acquiring a brain image to be classified;
the second acquisition module is used for inputting the brain image into a spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image;
and the classification module is used for inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image.
In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the following steps when executing the computer program:
acquiring a brain image to be classified;
inputting the brain image into a spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image;
and inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
acquiring a brain image to be classified;
inputting the brain image into a spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image;
and inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image.
In the method and apparatus for classifying a brain image, the computer device and the readable storage medium provided in the above embodiments, the computer device obtains a brain image to be classified, inputs the brain image into the spherical mapping model to obtain a cortical spherical mapping map corresponding to the brain image, and inputs the cortical spherical mapping map into the spherical convolution network to obtain a classification result of the brain image. In the method, the computer equipment can accurately map the characteristics of the brain image to be classified into the cerebral cortex spherical mapping map through the spherical mapping model, so that the accuracy of the cerebral cortex spherical mapping map corresponding to the obtained brain image to be classified is improved, and the obtained cerebral cortex spherical mapping map can accurately display the structural change of the cerebral cortex of the brain image to be classified, so that the spherical convolution network can more accurately extract the characteristics of the cerebral cortex of the brain image to be classified, the brain image to be classified is more accurately classified, and the obtained classification result of the brain image to be classified is improved.
Drawings
FIG. 1 is a schematic diagram of an internal structure of a computer device according to an embodiment;
fig. 2 is a flowchart illustrating a method for classifying a brain image according to an embodiment;
fig. 3 is a schematic flowchart of obtaining a classification result of a brain image to be classified according to an embodiment;
fig. 4 is a flowchart illustrating a method for classifying a brain image according to another embodiment;
fig. 5 is a flowchart illustrating a method for classifying a brain image according to another embodiment;
fig. 6 is a flowchart illustrating a method for classifying a brain image according to another embodiment;
fig. 7 is a schematic structural diagram of a brain image classification device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The brain image classification method provided by the embodiment of the application can be applied to computer equipment shown in fig. 1. The computer device comprises a processor and a memory connected by a system bus, wherein a computer program is stored in the memory, and the steps of the method embodiments described below can be executed when the processor executes the computer program. Optionally, the computer device may further comprise a network interface, a display screen and an input device. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a nonvolatile storage medium storing an operating system and a computer program, and an internal memory. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. Optionally, the computer device may be a server, a personal computer, a personal digital assistant, other terminal devices such as a tablet computer, a mobile phone, and the like, or a cloud or a remote server, and the specific form of the computer device is not limited in the embodiment of the present application.
Taking Alzheimer's Disease (AD) as an example, AD is one of the most common neurodegenerative diseases, and because Magnetic Resonance Imaging (MRI) has the characteristics of no radioactivity, high Imaging quality of brain structures and the like, it is widely applied to Diagnosis of AD and related diseases, which makes the need for Computer Aided Detection (CAD) urgent. The computer-aided diagnosis of AD can effectively screen AD patients and early Mild Cognitive Impairment (MCI) stages thereof according to medical images such as MRI and the like, thereby greatly reducing the workload of doctors and simultaneously improving the detection accuracy of the doctors. The main problems faced by the existing CAD are low automation degree, low detection precision and low speed, and the CAD processing usually uses a manually designed feature extractor to extract features, which may lose much useful information in the process and limit the learning ability of the model greatly. The three-dimensional based deep fully convolutional neural network allows all information including the whole lesion region and the surrounding structures thereof to be processed, the correlation among different layers is considered, the representation of a plurality of abstract levels of data is automatically acquired, useful features can be amplified, and therefore the neuropsychiatric diseases can be efficiently and accurately diagnosed.
The following describes the technical solution of the present invention and how to solve the above technical problems with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a flowchart illustrating a method for classifying a brain image according to an embodiment. Fig. 3 is a schematic flowchart of obtaining a classification result of a brain image to be classified according to an embodiment. The embodiment relates to a specific implementation process of inputting a brain image to be classified into a spherical mapping model by computer equipment to obtain a cerebral cortex spherical mapping map corresponding to the brain image to be classified, and inputting the obtained cerebral cortex spherical mapping map into a spherical convolution network to obtain a classification result of the brain image to be classified. As shown in fig. 2, the method may include:
s201, obtaining a brain image to be classified.
Wherein the brain image to be classified is a medical image of the patient's brain acquired by a medical imaging device. Alternatively, the brain image to be classified may be a Computed Tomography (CT) image of the brain, such as a T1-weighted image or a T2-weighted image, an MRI image of the brain, or other medical images of the brain. Alternatively, the computer device may acquire the brain image to be classified from a PACS (Picture Archiving and Communication Systems) server, or may acquire the brain image to be classified in real time from a medical imaging device. Optionally, after the computer device acquires the brain image to be classified, the computer device may perform preprocessing on the brain image to be classified to remove the medical imageAnd the influence of the prepared working condition on the quality of the obtained image provides a high-quality image for subsequent analysis, wherein the preprocessing comprises resampling processing, size adjustment processing, head and bone removing processing, image non-uniform correction processing, histogram matching processing, gray level normalization processing and the like. It should be noted that the preprocessed brain image to be classified has an image size of 256 × 256 × 256mm3The directions are all standard Cartesian LPI coordinate systems, and the gray scale ranges are all standard images in the (-1,1) interval.
S202, inputting the brain image into the spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image.
Specifically, the computer device inputs the acquired brain image to be classified into the spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image. The obtained cerebral cortex spherical mapping image corresponding to the brain image to be classified comprises sulci and gyrus information of the brain image to be classified.
S203, inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image.
Specifically, the computer device inputs the obtained cerebral cortex spherical mapping map into a spherical convolution network to obtain a classification result of the cerebral image to be classified. Optionally, the network structure of the spherical convolutional network may be any one of a three-dimensional dense connection network structure, a spherical residual error network structure, and a three-dimensional dense connection network combined feature weighting module network structure. Optionally, the obtained classification result of the brain image to be classified may include AD, stable mild cognitive impairment (stmci), progressive mild cognitive impairment (pMCI), and normality, and it should be noted that the obtained classification result of the brain image to be classified further includes a probability prediction index for each classification category, for example, the obtained classification result of a certain brain image to be classified may be: and (3) normal: 5%, sMCI: 7%, pMCI: 20%, AD: 68 percent.
In this embodiment, the computer device can accurately map the features of the brain image to be classified into the cortical spherical mapping map through the spherical mapping model, thereby improving the accuracy of the cortical spherical mapping map corresponding to the obtained brain image to be classified, and because the obtained cortical spherical mapping map can accurately display the structural change of the cortex of the brain image to be classified, the spherical convolution network can more accurately extract the features of the cortex of the brain image to be classified, thereby classifying the brain image to be classified more accurately, and improving the classification result of the obtained brain image to be classified.
Fig. 4 is a flowchart illustrating a method for classifying a brain image according to another embodiment. The embodiment relates to a specific implementation process of inputting a brain image to be classified into a spherical mapping model by computer equipment to obtain a cerebral cortex spherical mapping map corresponding to the brain image to be classified. As shown in fig. 4, on the basis of the foregoing embodiment, as an optional implementation manner, the foregoing S202 includes:
s401, inputting the brain image into the spherical mapping model to obtain a cerebral cortex spherical expansion image corresponding to the brain image.
Specifically, the computer device inputs the brain image to be classified into the spherical mapping model to obtain a cerebral cortex spherical expansion map corresponding to the brain image to be classified. Optionally, the computer device may input the brain image to be classified into the spherical mapping model, and expand the brain image to be classified according to a preset expansion rule to obtain a cortical spherical expansion map corresponding to the brain image to be classified. Optionally, the spherical mapping model includes a spherical mapping algorithm or a spherical mapping network. Alternatively, the preset unfolding rule may be the unfolding rule used in the freesurfer segmentation tool.
S402, mapping the cerebral cortex spherical expansion map to a preset standard spherical surface to obtain a cerebral cortex spherical mapping map corresponding to the cerebral image.
Specifically, after obtaining a cortical spherical expansion map corresponding to the brain image to be classified, the computer device maps the obtained cortical spherical expansion map onto a preset standard spherical surface according to an inverse process of the preset expansion rule, so as to obtain a cortical spherical mapping map corresponding to the brain image to be classified. It should be noted that the process of the computer device mapping the cortical spherical expansion map to the preset standard spherical surface is a process of mapping the sulcus gyrus information of the brain image to be classified to the preset standard spherical surface.
In this embodiment, the computer device inputs the brain image to be classified into the spherical mapping model, obtains the cortical spherical expansion map corresponding to the brain image to be classified, and maps the cortical spherical expansion map corresponding to the brain image to be classified onto a preset standard spherical surface, thereby obtaining the cortical spherical mapping map corresponding to the brain image to be classified.
Fig. 5 is a flowchart illustrating a method for classifying a brain image according to another embodiment. The embodiment relates to a specific implementation process of training a spherical mapping model by computer equipment when the spherical mapping model is a spherical mapping network. As shown in fig. 5, on the basis of the foregoing embodiment, as an alternative implementation, the training process of the spherical mapping model may include:
s501, obtaining a sample brain image.
Wherein the sample brain image is a sample medical image of the patient's brain acquired by the medical imaging device. Alternatively, the sample brain image may be a Computed Tomography (CT) image of the brain, such as a T1-weighted image or a T2-weighted image, or an MRI image of the brain, or other medical images of the brain. Alternatively, the computer device may acquire the sample brain image from a PACS (Picture Archiving and Communication Systems) server, or may acquire the sample brain image from a medical imaging device in real time. Optionally, after the computer device acquires the sample brain image, the sample brain image may be preprocessed to remove the influence of the working condition of the medical imaging device on the quality of the acquired image, and the result is the subsequent scoreAnd analyzing to provide high-quality images, wherein the preprocessing comprises resampling processing, resizing processing, removing bones, image non-uniformity correction processing, histogram matching processing, gray level normalization processing and the like. It should be noted that the preprocessed sample brain image has an image size of 256 × 256 × 256mm3The directions are all standard Cartesian LPI coordinate systems, and the gray scale ranges are all standard images in the (-1,1) interval.
S502, inputting the sample brain image into a preset spherical mapping network to obtain a sample cerebral cortex spherical mapping image corresponding to the sample brain image;
specifically, the computer device inputs the sample brain image into a preset spherical mapping network to obtain a sample cortical spherical expansion map corresponding to the sample brain image, and maps the obtained sample cortical spherical expansion map onto a preset standard spherical surface to obtain a sample cortical spherical mapping map corresponding to the sample brain image. Optionally, the preset network structure of the spherical mapping network may be a V-Net network structure.
S503, training the preset spherical mapping network according to the sample cortical spherical mapping image and the preset reference cortical spherical mapping image to obtain the spherical mapping network.
Specifically, after obtaining a sample cortical spherical mapping map corresponding to the sample brain image, the computer device obtains a loss function value of a preset spherical mapping network according to the sample cortical spherical mapping map and a preset reference cortical spherical mapping map, trains the preset spherical mapping network according to the loss function value of the preset spherical mapping network, and determines the corresponding preset spherical mapping network as the spherical mapping network until the loss function value of the preset spherical mapping network reaches a stable value.
In this embodiment, the computer device obtains a sample cortical spherical mapping map corresponding to the sample brain image by inputting the sample brain image into the preset spherical mapping network, trains the preset spherical mapping network according to the sample cortical spherical mapping map and the preset reference cortical spherical mapping map, and can train the preset spherical mapping network more accurately through a large number of sample brain images, thereby improving the accuracy of the obtained spherical mapping network.
On the basis of the foregoing embodiment, as an optional implementation manner, before training a preset spherical mapping network according to a sample cortical spherical mapping map and a preset reference cortical spherical mapping map to obtain the spherical mapping network, the method further includes: and processing the sample brain image by using a preset brain image segmentation tool to obtain a preset reference cerebral cortex spherical mapping image.
Specifically, the computer device processes the sample brain image by using a preset brain image segmentation tool to obtain a preset reference cortical spherical mapping image. Optionally, the preset brain image segmentation tool may be a freesurfer segmentation tool, or may be another segmentation tool for processing a brain image. In this embodiment, the computer device processes the sample brain image by using the preset brain image segmentation tool, and can quickly obtain the preset reference cortical spherical mapping map corresponding to the sample brain image, thereby improving the efficiency of obtaining the preset reference cortical spherical mapping map, and further improving the efficiency of training the preset spherical mapping network according to the obtained sample cortical spherical mapping map and the preset reference cortical spherical mapping map, and obtaining the spherical mapping network.
Fig. 6 is a flowchart illustrating a method for classifying a brain image according to another embodiment. The embodiment relates to a specific implementation process for training a spherical convolutional network by computer equipment. As shown in fig. 6, on the basis of the foregoing embodiment, as an alternative implementation, the training process of the spherical convolutional network may include:
s601, obtaining a sample cerebral cortex spherical mapping image.
Specifically, the computer device first obtains a sample cortical spherical map corresponding to the sample brain image. Optionally, the computer device may input the sample brain image into the spherical mapping model to obtain a sample cortical spherical mapping map corresponding to the sample brain image.
S602, inputting the sample cerebral cortex spherical mapping image into a preset spherical convolution network to obtain a sample classification result of the sample cerebral image corresponding to the sample cerebral cortex spherical mapping image.
Specifically, the computer device inputs the obtained sample cortical spherical mapping map into a preset spherical convolution network to obtain a sample classification result of the sample brain image corresponding to the sample cortical spherical mapping map. The sample classification results of the sample brain images corresponding to the sample cortical spherical mapping map may include AD, stable mild cognitive impairment (stmci), progressive mild cognitive impairment (pMCI), and normality, and it should be noted that the obtained sample classification results of the sample brain images corresponding to the sample cortical spherical mapping map further include probability prediction indexes for each classification category, for example, the obtained sample classification results of the sample brain images corresponding to a certain sample cortical spherical mapping map may be: and (3) normal: 70%, sMCI: 20%, pMCI: 7%, AD: 3 percent.
And S603, training a preset spherical convolution network according to the sample classification result and a label marked on the sample brain image corresponding to the sample cerebral cortex spherical mapping image in advance to obtain the spherical convolution network.
Specifically, the computer device obtains a loss function value of a preset spherical convolution network according to the obtained sample classification result of the sample brain image corresponding to the sample cortical spherical mapping image and a label marked on the sample brain image corresponding to the sample cortical spherical mapping image in advance, trains the preset spherical convolution network according to the loss function value of the preset spherical convolution network, and determines the corresponding preset spherical convolution network as the spherical convolution network until the loss function value of the preset spherical convolution network reaches a stable value. Optionally, the label of the sample brain image mark corresponding to the sample cortical spherical map may be normal, or may be stmci, or may be pMCI. For example, when the label of the sample brain image label corresponding to the sample cortical spherical map is normal, the sample cortical spherical map is input into a preset spherical convolution network, and when the obtained sample classification result is: and (3) normal: 96%, sMCI: 5%, pMCI: 2%, AD: when the loss function value of the preset spherical convolution network is 3%, the value of the obtained loss function of the preset spherical convolution network is 0.05, when the result obtained when the parameters of the preset spherical convolution network are changed is not changed any more, the value of the loss function of the preset spherical convolution network reaches a stable value, and the corresponding preset spherical convolution network when the value of the loss function is 0.05 is determined as the spherical convolution network by the computer equipment.
In this embodiment, the computer device obtains a sample classification result of a sample brain image corresponding to a sample cortical spherical mapping image by inputting the sample cortical spherical mapping image into a preset spherical convolution network, trains the preset spherical convolution network according to the obtained sample classification result and a label labeled in advance for the sample brain image corresponding to the sample cortical spherical mapping image, and can train the preset spherical convolution network more accurately through a large number of sample cortical spherical mapping images, thereby improving the accuracy of the obtained spherical convolution network.
It should be understood that although the various steps in the flow charts of fig. 2-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-6 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
Fig. 7 is a schematic structural diagram of a brain image classification device according to an embodiment. As shown in fig. 7, the apparatus may include: a first acquisition module 10, a second acquisition module 11 and a classification module 12.
Specifically, the first obtaining module 10 is configured to obtain a brain image to be classified;
the second obtaining module 11 is configured to input the brain image into the spherical mapping model to obtain a cerebral cortex spherical mapping map corresponding to the brain image;
and the classification module 12 is used for inputting the cerebral cortex spherical mapping image into the spherical convolution network to obtain a classification result of the cerebral image.
Optionally, the network structure of the spherical convolutional network is any one of the following: a three-dimensional dense connection network structure; a spherical residual network structure; a three-dimensional dense connection network incorporates a feature weighting module network structure.
Optionally, the spherical mapping model includes a spherical mapping algorithm or a spherical mapping network.
The brain image classification device provided in this embodiment may implement the method embodiments described above, and the implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, the second obtaining module 11 includes: a first acquisition unit and a second acquisition unit.
Specifically, the first obtaining unit is configured to input the brain image into the spherical mapping model to obtain a cerebral cortex spherical expansion map corresponding to the brain image;
and the second acquisition unit is used for mapping the cerebral cortex spherical expansion map to a preset standard spherical surface to obtain a cerebral cortex spherical mapping map corresponding to the cerebral image.
The brain image classification device provided in this embodiment may implement the method embodiments described above, and the implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, the first obtaining unit is specifically configured to input the brain image into the spherical mapping model, and expand the brain image according to a preset expansion rule to obtain a cortical spherical expansion map corresponding to the brain image.
The brain image classification device provided in this embodiment may implement the method embodiments described above, and the implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, when the spherical mapping model is a spherical mapping network, optionally, the apparatus further includes: the device comprises a third acquisition module, a fourth acquisition module and a first training module.
Specifically, the third acquisition module is used for acquiring a sample brain image;
the fourth acquisition module is used for inputting the sample brain image into a preset spherical mapping network to obtain a sample cerebral cortex spherical mapping image corresponding to the sample brain image;
the first training module is used for training the preset spherical mapping network according to the sample cortical spherical mapping image and the preset reference cortical spherical mapping image to obtain the spherical mapping network.
The brain image classification device provided in this embodiment may implement the method embodiments described above, and the implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, the apparatus further includes: and a fifth obtaining module.
Specifically, the fifth obtaining module is configured to process the sample brain image by using a preset brain image segmentation tool, so as to obtain a preset reference cortical spherical map.
The brain image classification device provided in this embodiment may implement the method embodiments described above, and the implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, the apparatus further includes: the device comprises a sixth acquisition module, a seventh acquisition module and a second training module.
Specifically, the sixth obtaining module is used for obtaining a sample cerebral cortex spherical mapping map;
a seventh obtaining module, configured to input the sample cortical spherical map into a preset spherical convolution network, so as to obtain a sample classification result of the sample brain image corresponding to the sample cortical spherical map;
and the second training module is used for training the preset spherical convolution network according to the sample classification result and the label which is marked on the sample brain image corresponding to the sample cerebral cortex spherical mapping image in advance to obtain the spherical convolution network.
The brain image classification device provided in this embodiment may implement the method embodiments described above, and the implementation principle and technical effect are similar, which are not described herein again.
For specific definition of the brain image classification device, reference may be made to the above definition of the brain image classification method, which is not described herein again. The modules in the brain image classification device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring a brain image to be classified;
inputting the brain image into the spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image;
and inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image.
The implementation principle and technical effect of the computer device provided by the above embodiment are similar to those of the above method embodiment, and are not described herein again.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a brain image to be classified;
inputting the brain image into the spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image;
and inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of classifying a brain image, the method comprising:
acquiring a brain image to be classified;
inputting the brain image into a spherical mapping model to obtain a cerebral cortex spherical mapping image corresponding to the brain image;
and inputting the cerebral cortex spherical mapping image into a spherical convolution network to obtain a classification result of the cerebral image.
2. The method of claim 1, wherein the inputting the brain image into a spherical mapping model to obtain a cortical spherical map corresponding to the brain image comprises:
inputting the brain image into the spherical mapping model to obtain a cerebral cortex spherical expansion image corresponding to the brain image;
and mapping the cerebral cortex spherical expansion map to a preset standard spherical surface to obtain a cerebral cortex spherical mapping map corresponding to the cerebral image.
3. The method of claim 2, wherein the inputting the brain image into a spherical mapping model to obtain a cortical spherical expansion map corresponding to the brain image comprises:
and inputting the brain image into the spherical mapping model, and unfolding the brain image according to a preset unfolding rule to obtain a cerebral cortex spherical unfolding image corresponding to the brain image.
4. The method of claim 1, wherein the network structure of the spherical convolutional network is any one of: a three-dimensional dense connection network structure; a spherical residual network structure; a three-dimensional dense connection network incorporates a feature weighting module network structure.
5. The method of claim 1, wherein the spherical mapping model comprises a spherical mapping algorithm or a spherical mapping network.
6. The method of claim 5, wherein when the spherical mapping model is a spherical mapping network, the training process of the spherical mapping model comprises:
obtaining a sample brain image;
inputting the sample brain image into a preset spherical mapping network to obtain a sample cerebral cortex spherical mapping image corresponding to the sample brain image;
and training the preset spherical mapping network according to the sample cortical spherical mapping map and a preset reference cortical spherical mapping map to obtain the spherical mapping network.
7. The method of claim 6, wherein the training of the pre-determined spherical mapping network based on the sample cortical spherical mapping map and a pre-determined reference cortical spherical mapping map further comprises:
and processing the sample brain image by using a preset brain image segmentation tool to obtain the preset reference cerebral cortex spherical mapping image.
8. The method of claim 1, wherein the training process of the spherical convolutional network comprises:
obtaining a sample cerebral cortex spherical mapping map;
inputting the sample cerebral cortex spherical mapping map into a preset spherical convolution network to obtain a sample classification result of a sample cerebral image corresponding to the sample cerebral cortex spherical mapping map;
and training the preset spherical convolution network according to the sample classification result and a label which is labeled on the sample brain image corresponding to the sample cerebral cortex spherical mapping image in advance to obtain the spherical convolution network.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method according to any of claims 1-8.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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