Alzheimer's disease classification method and system based on anatomical landmark and residual network

Abstract

The invention discloses an Alzheimer's disease classification method and system based on an anatomical landmark and a residual network, and the method comprises the steps: carrying out the tissue segmentation modulation of a training image set, and obtaining a gray matter image; comparing voxels of the Alzheimer's disease image and the normal subject image in the training image set to identify ananatomical landmark; taking the obtained anatomical landmark as a center, and extracting grey matter blocks of the gray image; and connecting the grey matter blocks, inputting the obtained synthetic blocks into a residual error network model for feature extraction, taking features extracted by the residual error network model as input of a classifier, and classifying Alzheimer's disease patients and normal subjects in the test image set. The anatomical landmark is taken as the center, feature blocks of gray matters in three tissues of the brain are taken as the input of the residual network, the feature blocks are taken as the feature expression of each MR image, the residual network model is adopted to enhance the feature learning capability of the network, and the classification accuracyis enhanced.

Description

technical field [0001] The present disclosure relates to the technical fields of artificial intelligence and medical images, in particular to a method and system for classifying Alzheimer's disease based on anatomical landmarks and residual networks. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Magnetic resonance images (MRI) can clearly display the gray matter and white matter of the brain, and sensitively display changes in brain structure. There may be no obvious clinical symptoms in the early stage of the disease, but degenerative lesions must occur in some relevant brain regions. Therefore, MRI has now become an indispensable auxiliary system in the diagnosis of various neurological diseases. [0004] At present, the diagnosis of Alzheimer's disease (AD) based on MRI images is mainly divided into three types: [0005] (1) Metho...

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Problems solved by technology

[0009] However, the inventors have found that at least the following problems exist in the prior art: the methods based on regions of interest (ROIs) rely on the total changes of each brain area to Diagnosis without taking into account their subtle changes; voxel-based methods produce features with very high dimensionality, time-consuming classification and often challenged by overfitting; landmark-based depth The learning method conducts independent training for each landmark-based block. When the classification results of the blocks are quite different, the blocks with low classification accuracy will affect the final diagnosis result.

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