Depression recognition system fusing cerebral cortex thickness and edge system morphological features

Abstract

The invention provides a depression recognition system fusing the cerebral cortex thickness and the edge system morphological features. According to the present invention, the cortex thickness features of a brain is fused with the morphological features of hippocampus and almond kernels of an edge system to carry out the specific feature selection, and a deep neural network (DNN) is used to fuse the features and classify, so that the accuracy of utilizing an MRI image to recognize the depression is remarkably improved. The system comprises (a) an MRI image acquisition module used for acquiringthe brain MRI data of a testee; (b) a feature selection module used for acquiring the cerebral cortex thickness features of the brain and the morphological features of hippocampus and almond kernelsof the edge system according to the acquired MRI data, and further selecting the specific features for recognizing the depression; and (c) a classification and recognition module which uses the deep neural network (DNN) to fuse the cerebral cortex thickness features of the brain and morphological features of the edge system, classify, and recognize the depressive patients and the normal testee.

Description

technical field [0001] The invention relates to the technical fields of brain neuroscience, medical imaging, and machine learning, in particular to a depression recognition system that integrates cerebral cortex thickness and limbic system morphological features. Background technique [0002] Depression (MDD) is the fourth leading disease in the world, and patients will have clinical manifestations such as low mood, slow thinking and cognitive impairment. As a mental disease whose number is increasing year by year and whose influence is gradually expanding, depression has gradually attracted people's attention. Common to other psychiatric diseases (such as Alzheimer's disease, mild cognitive impairment, etc.), the brain structure of patients with depression will undergo a series of changes, including atrophy. Although there have been many studies on brain neuroimaging in recent years, compared with other mental diseases, research on depression has not yet achieved good resu...

AI Technical Summary

Problems solved by technology

At present, most studies on MRI images of brain structures in depression focus on the significant differences between certain specific brain structures and normal controls

The MRI image classification research on depression mostly uses the cortical thickness calculated by FreeSurfer as a feature, or the volume of the subcortical hippocampus, frontal lobe, temporal lobe, cingulate gyrus, etc. to conduct research. When classifying images Accuracy has been low

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