Image Analysis Of Cerebral Cortex And Subcortical Nuclei In Alzheimer's Disease Based On Cross-two-stream Feature Fusion Registration Networ

Alzheimer’s disease(AD)is a progressive and irreversible neurodegenerative disease characterized by memory loss and cognitive impairment.About 90 million people worldwide have AD today,and the number of people with AD is expected to reach 115 million by 2050.Although the progression of AD can be delayed,there are currently no effective treatments to stop or reverse AD,therefore,early diagnosis and screening of AD is essential for the prevention and intervention of the condition.The clinical diagnostic procedure for AD can be performed by a lumbar puncture to detect specific biomarkers in the cerebrospinal fluid(CSF).However,this is an invasive test that poses risks to the patient.Another common biological marker of AD progression is morphological changes in brain structure,and with the development of neuroimaging technology,it is of irreplaceable value for accurate diagnosis and early detection of AD.Magnetic resonance imaging(MRI)can noninvasively capture the internal structure and atrophy of the brain,helping us to understand the anatomical and functional changes in the brain associated with AD.In particular,T1-weighted imaging(T1WI)provides detailed information about the internal anatomical structure and morphology of brain tissue,allowing the detection and tracking of the evolution of brain atrophy in AD.Indeed,one of the distinguishing features of AD diagnosis is temporal lobe atrophy,especially of specific subcortical structures such as hippocampus and amygdala.Accurate segmentation of brain anatomy lays the foundation for cohort studies of healthy and brain disease populations,especially for subsequent feature extraction,analysis,and construction of disease classification models.In addition,for a given data set population specific human brain MRI templates can be constructed to provide a standardized reference between healthy and diseased populations for accurate neuroanatomical localization,structural and functional comparisons for neuroscience development and clinical research.Therefore,the research idea of this paper is divided into the following two parts:(1)Using image registration algorithm for label migration to achieve brain structure segmentation.(2)Construction of a brain template generation network to generate brain templates of the sample population.In the first part of the work,an intersected dual stream based multiscale attentional feature fusion network,named MAFF-Net,is proposed in this paper for diffeomorphic registration of brain images.Firstly,the intersected dual stream network is used to infer the mutual mapping relationship between image pairs,and fuse the high and low semantic information of multiscale features by introducing the attention mechanism,and finally,the diffeomorphic registration is used to enhance the continuity and global smoothness of the deformation field to improve the alignment quality.The experimental results on the inhouse,OASIS-AD and OASIS-Health datasets show that the MAFF-Net algorithm has mean values of 83.2%,85.3%and 86.5%for the anatomical structure Dice similarity coefficient,mean values of 0.027%,0.192%and 0.089%for the negative Jacobi determinant voxel ratio,mean values of 92.4%,90.9%,and 92.0%for Recall,and mean values of 0.447 mm,0.387 mm,and 0.345 mm for ASD on the three test sets respectively.All metrics except Recall were better than the comparison algorithm.This paper further uses the segmentation results of the OASIS dataset to analyze the volume and surface area of the cerebral cortex,hippocampus,and amygdala in relation to age,and to explore the close relationship between the degree of atrophy of these brain structures and AD.In the second part of the work,a brain template construction network named Template-Net is proposed in this paper,which is based on MAFF-Net,using brain templates as an intermediate state bridge and adding the similarity loss function between individual images to construct a more accurate alignment network,while generating accurate brain template images using the given dataset.The experimental results on OASIS-Health and OASIS-AD datasets show that the Template-Net designed in this paper achieves better results in all metrics compared to MAFF-Net.In this paper,we further compare the morphological brain structures of the constructed healthy and AD brain templates in order to explore the AD-related imaging markers and more accurately assess the variability between the disease group and the healthy population.