CN112767341A - Deep learning-based method for classifying cognitive impairment of brain structure of type 2 diabetes patient - Google Patents
Deep learning-based method for classifying cognitive impairment of brain structure of type 2 diabetes patient Download PDFInfo
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Abstract
The invention relates to a deep learning-based method for classifying cognitive impairment of brain structures of type 2 diabetes patients, which comprises the following steps: extracting a whole brain 3D image for marking the brain cognitive disorder, and carrying out brain peeling treatment to obtain a brain region image corresponding to the brain cognitive disorder; constructing a 3D CNN network model with 11 layers, inputting the brain region image serving as a training set into the 3D CNN network model for training, and adjusting network parameters and functions until an output result is an accurate classification result; and inputting the real-time whole brain 3D image into the trained 3DCNN network model, and outputting a result serving as a classification result of whether the input whole brain 3D image has cognitive impairment or not through calculation of the network model. By the method and the device, the influence of the brain structures on cognitive impairment can be accurately identified.
Description
Technical Field
The invention relates to the technical field of deep learning, in particular to a method for classifying cognitive impairment of brain structures of type 2 diabetes patients based on deep learning.
Background
Previous studies have consistently shown an association between diabetes and cognitive decline. Both type 1 and type 2 diabetes are associated with decreased psychological speed and psychological flexibility; type 2 diabetes is also associated with impaired learning and memory.
Based on the results, scientists propose to compare the cognitive function decline of the type 2 diabetes mellitus patients with the cognitive abilities of normal people and people at risk of Alzheimer's disease, and research and evaluate the cognitive function of the diabetes mellitus outpatient. Subjects first received a simple memory screen and then a comprehensive memory assessment. The subject's memory, attention, language fluency and performance are assessed using the NACC unified dataset, and a specific cognitive diagnosis is given to the patient by a team of neuropsychologists and geriatric physicians through these results. Subjects were compared to normal controls and patients with mild cognitive impairment from amnesic (aMCI) in the ADRC database.
The results showed that 30 patients were confirmed in the group and were aged (64.4. + -. 7.4). Diabetic patients performed significantly worse on neuropsychological testing compared to normal cognitive functioning populations.
Type 2 diabetes patients develop cognitive impairment earlier than their peers as the disease progresses and are associated with susceptibility to gene polymorphisms, and the brain also shows aging phenomena.
There are many documents that show that the existence of brain cognitive disorder is closely related to brain structure, but it is still a problem to be solved that structural changes in which part of the brain can seriously affect the cognitive function of a human to form cognitive disorder.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for classifying the cognitive impairment of the brain structure of type 2 diabetes patients based on deep learning, aiming at the defects of the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: constructing a classification method of cognitive impairment of brain structures of type 2 diabetes patients based on deep learning, comprising the following steps:
extracting a whole brain 3D image for marking the brain cognitive disorder, carrying out brain peeling treatment, obtaining a brain region image corresponding to the brain cognitive disorder, and dividing a training set and a test set;
constructing 11 layers of 3D CNN network models, inputting the brain region images serving as training sets into the 3D CNN network models for training, adjusting network parameters and functions until the output results are accurate classification results, inputting the brain region images serving as test sets into the 3D CNN network models after training is finished, and verifying the accuracy of the 3D CNN network models;
and inputting the real-time whole brain 3D image into the trained 3D CNN network model, and outputting a result serving as a classification result of whether the input whole brain 3D image has cognitive disorder or not through calculation of the network model.
Wherein, in the step of acquiring the brain regional image corresponding to the brain cognitive impairment, the method comprises the following steps:
acquiring the gravity center position of a brain structure in a whole brain 3D image through an image processing means;
taking the gravity center position of the brain structure as a coordinate origin, constructing a space three-dimensional coordinate system, wherein the Z axis is the center position of the left half brain and the right half brain, and establishing an X axis and a Y axis according to the Z axis;
after the barycenter and the coordinate system are determined, in the range of 64 voxels from 32 voxels before the barycenter to 31 voxels after the barycenter in the X-axis direction, in the range of 10 voxels from 30 voxels below the barycenter to 39 voxels below the barycenter in the Y-axis direction, and in the range of 64 voxels left and right of the barycenter in the Z-axis direction, an image of 64X 10 is extracted as a brain region image.
After the step of obtaining the brain regional image corresponding to the brain cognitive disorder, the method further comprises a step of enhancing data of the training set; amplification was performed by a 3D rotation operation to 10 times the original data amount.
After the step of acquiring the brain regional image corresponding to the brain cognitive impairment, the method further comprises the step of image preprocessing, and the method comprises the following steps of:
grayscale normalization, including window width, window level and pixel normalization.
And observing the loss curves of the training set and the testing set through the training curve, wherein if the loss curves show a descending trend, the learning success rate of the 3D CNN network model generally shows an ascending trend.
Wherein, in the step of judging the accuracy of the trained network, the degree of development of cognitive impairment of type 2 diabetic patients is assessed by LDH and FA values.
In the step of obtaining the brain region image corresponding to the brain cognitive disorder, 13 brain image markers of the cognitive disorder caused by type 2 diabetes brain aging comprise cingulate gyrus, right frontal lobe, left parietal lobe, lumbricus, bilateral thalamus, right temporal gyrus which are positioned in the brain region with abnormal change of white matter microstructure, and frontal lobe, parietal lobe, temporal lobe, thalamus, basal ganglia, cerebellar hemisphere and brainstem which are positioned in the brain region with weakened functional connection.
The invention provides a deep learning-based method for classifying cognitive impairment of brain structures of type 2 diabetes patients, which comprises the following steps: extracting a whole brain 3D image for marking the brain cognitive disorder, carrying out brain peeling treatment, obtaining a brain region image corresponding to the brain cognitive disorder, and dividing a training set and a test set; constructing 11 layers of 3D CNN network models, inputting brain region images serving as training sets into the 3D CNN network models for training, adjusting network parameters and functions until output results are accurate classification results, inputting the brain region images serving as test sets into the 3D CNN network models after training is finished, and verifying the accuracy of the 3D CNN network models; and inputting the real-time whole brain 3D image into the trained 3D CNN network model, and outputting a result as a classification result of whether the input brain 3D image has cognitive disorder or not through calculation of the network model. By the method and the device, the influence of the brain structures on cognitive impairment can be accurately identified.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method for classifying cognitive impairment of brain structures of type 2 diabetic patients based on deep learning according to the present invention.
Fig. 2 is a curve diagram of classification results and losses for a training set and a test set in a deep learning-based method for classifying cognitive impairment of brain structures of type 2 diabetic patients according to the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a method for classifying brain structure recognition disorders of type 2 diabetic patients based on deep learning, comprising:
extracting a whole brain 3D image for marking the brain cognitive disorder, carrying out brain peeling treatment, obtaining a brain region image corresponding to the brain cognitive disorder, and dividing a training set and a test set;
constructing 11 layers of 3D CNN network models, inputting the brain region images serving as training sets into the 3D CNN network models for training, adjusting network parameters and functions until the output results are accurate classification results, inputting the brain region images serving as test sets into the 3D CNN network models after training is finished, and verifying the accuracy of the 3D CNN network models;
and inputting the real-time whole brain 3D image into the trained 3D CNN network model, and outputting a result serving as a classification result of whether the input whole brain 3D image has cognitive disorder or not through calculation of the network model.
Wherein, in the step of acquiring the brain regional image corresponding to the brain cognitive impairment, the method comprises the following steps:
acquiring the gravity center position of a brain structure in a whole brain 3D image through an image processing means;
taking the gravity center position of the brain structure as a coordinate origin, constructing a space three-dimensional coordinate system, wherein the Z axis is the center position of the left half brain and the right half brain, and establishing an X axis and a Y axis according to the Z axis;
after the barycenter and the coordinate system are determined, in the range of 64 voxels from 32 voxels before the barycenter to 31 voxels after the barycenter in the X-axis direction, in the range of 10 voxels from 30 voxels below the barycenter to 39 voxels below the barycenter in the Y-axis direction, and in the range of 64 voxels left and right of the barycenter in the Z-axis direction, an image of 64X 10 is extracted as a brain region image.
After the step of obtaining the brain regional image corresponding to the brain cognitive disorder, the method further comprises a step of enhancing data of the training set; amplification was performed by a 3D rotation operation to 10 times the original data amount.
After the step of acquiring the brain regional image corresponding to the brain cognitive impairment, the method further comprises the step of image preprocessing, and the method comprises the following steps of:
grayscale normalization, including window width, window level and pixel normalization.
And observing the loss curves of the training set and the testing set through the training curve, wherein if the loss curves show a descending trend, the learning success rate of the 3D CNN network model generally shows an ascending trend.
Wherein, in the step of judging the accuracy of the trained network, the degree of development of cognitive impairment of type 2 diabetic patients is assessed by LDH and FA values.
In the step of obtaining the brain region image corresponding to the brain cognitive disorder, 13 brain image markers of the cognitive disorder caused by type 2 diabetes brain aging comprise cingulate gyrus, right frontal lobe, left parietal lobe, lumbricus, bilateral thalamus, right temporal gyrus which are positioned in the brain region with abnormal change of white matter microstructure, and frontal lobe, parietal lobe, temporal lobe, thalamus, basal ganglia, cerebellar hemisphere and brainstem which are positioned in the brain region with weakened functional connection.
An 11-layer 3d CNN network is constructed, and the network structure is as follows:
| Layer 1 | 64x 64x 10 | input |
| Layer 2 | 11 | conv |
| Layer 3 | 11 | batchNorm |
| Layer 4 | 17 | conv |
| Layer 5 | 17 | batchNorm |
| Layer 6 | 34 | conv |
| Layer 7 | 34 | batchNorm |
| Layer 8 | 1024 | fc |
| Layer 9 | 2 | |
| Layer | ||
| 10 | 2 | softmax |
| Layer 11 | 2 | output |
and observing the loss curves of the training set and the testing set through the training curve, wherein if the loss curves show a descending trend, the learning success rate of the 3D CNN network model generally shows an ascending trend. As shown in fig. 2.
The invention firstly discloses a T2DM susceptibility gene locus for brain aging. The neuroimaging and genetic mechanism of cognitive dysfunction of a T2DM patient is researched by adopting an advanced concept of imaging genetics and a brain network analysis frontier technology, susceptible gene loci of the cognitive dysfunction of the T2DM patient are extracted, and the occurrence of the cognitive dysfunction of the type 2 diabetic patient is closely related to the polymorphism of transmembrane protein 106B (TMEM106B), plasminogen activator and urokinase (PLAU) genes. The discovery of the susceptible gene locus can provide a biological marker for the diagnosis and the curative effect monitoring of early cognitive impairment of a T2DM patient, and is beneficial to the construction of a prediction model, so that the development of dementia is prevented or delayed, and the life quality of the patient is improved.
Different from the prior art, the invention provides a method for classifying the cognitive disorder of the brain structure of a type 2 diabetic patient based on deep learning, which comprises the following steps: extracting a brain 3D image for marking the cognitive disorder of the brain, carrying out brain peeling treatment, obtaining a brain region image corresponding to the cognitive disorder of the brain, and dividing a training set and a test set; constructing 11 layers of 3D CNN network models, inputting the brain region images serving as training sets into the 3D CNN network models for training, adjusting network parameters and functions until the output results are accurate classification results, inputting the brain region images serving as test sets into the 3D CNN network models after training is finished, and verifying the accuracy of the 3D CNN network models; and inputting the real-time brain 3D image into the trained 3D CNN network model, and outputting a result serving as a classification result of whether the input brain 3D image has cognitive impairment through calculation of the network model. By the method and the device, the influence of the brain structures on the cognitive disorder can be accurately identified.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (7)
1. A method for classifying cognitive impairment of brain structures of type 2 diabetes patients based on deep learning is characterized by comprising the following steps:
extracting a whole brain 3D image for marking the brain cognitive disorder, carrying out brain peeling treatment, obtaining a brain region image corresponding to the brain cognitive disorder, and dividing a training set and a test set;
constructing 11 layers of 3D CNN network models, inputting the brain region images serving as training sets into the 3D CNN network models for training, adjusting network parameters and functions until the output results are accurate classification results, inputting the brain region images serving as test sets into the 3D CNN network models after training is finished, and verifying the accuracy of the 3D CNN network models;
and inputting the real-time whole brain 3D image into the trained 3D CNN network model, and outputting a result serving as a classification result of whether the input whole brain 3D image has cognitive disorder or not through calculation of the network model.
2. The method for classifying cognitive impairment of brain structures of type 2 diabetes patients based on deep learning according to claim 1, wherein the step of acquiring brain region images corresponding to the cognitive impairment of the brain comprises the steps of:
acquiring the gravity center position of a brain structure in a whole brain 3D image through an image processing means;
taking the gravity center position of the brain structure as a coordinate origin, constructing a space three-dimensional coordinate system, wherein the Z axis is the center position of the left half brain and the right half brain, and establishing an X axis and a Y axis according to the Z axis;
after the barycenter and the coordinate system are determined, in the range of 64 voxels from 32 voxels before the barycenter to 31 voxels after the barycenter in the X-axis direction, in the range of 10 voxels from 30 voxels below the barycenter to 39 voxels below the barycenter in the Y-axis direction, and in the range of 64 voxels left and right of the barycenter in the Z-axis direction, an image of 64X 10 is extracted as a brain region image.
3. The deep learning based type 2 diabetic brain structure cognitive impairment classification method according to claim 1, characterized in that after the step of obtaining brain region images corresponding to brain cognitive impairment, a step of training set data enhancement is further included; amplification was performed by a 3D rotation operation to 10 times the original data amount.
4. The deep learning based classification method for cognitive impairment of brain structures of type 2 diabetic patients according to claim 1, further comprising a step of image preprocessing after the step of obtaining images of brain regions corresponding to cognitive impairment of brain, comprising:
grayscale normalization, including window width, window level and pixel normalization.
5. The method for classifying cognitive impairment of brain structure of type 2 diabetes patients based on deep learning of claim 1, wherein a loss curve of a training set and a testing set is observed through the training curve, and if the loss curve shows a downward trend, the learning success rate of the 3D CNN network model generally shows an upward trend.
6. The deep learning-based classification method for cognitive impairment of brain structure of type 2 diabetes mellitus patients according to claim 1, wherein in the step of judging the accuracy of the trained network, the degree of development of cognitive impairment of type 2 diabetes mellitus patients is evaluated by LDH and FA values.
7. The method for classifying cognitive impairment of brain structure of type 2 diabetes patients based on deep learning according to claim 1, wherein in the step of obtaining the image of the brain region corresponding to the cognitive impairment of brain, 13 brain image markers of cognitive impairment caused by brain aging of type 2 diabetes include cingulate gyrus, right frontal lobe, left parietal lobe, lumbricus, bilateral thalamus, right temporal gyrus, and frontal lobe, parietal lobe, temporal lobe, thalamus, basal ganglia, cerebellar hemisphere and brainstem in the brain region with weakened functional connection, where the abnormal change of white matter microstructure occurs.
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