Abstract: Abstract: Aiming at the problem that the existing hippocampal segmentation algorithm can not segment the target accurately， a novel hippocampal segmentation model based on feature fusion using codec structure is studied. Firstly， Resnet34 is used as the model feature encoding layer to extract richer semantic features; Secondly， the ASPP module based on mixed expansion convolution is introduced into the coding and decoding transition layer to obtain multi-scale feature information. Finally， the attention feature fusion mechanism is used as the connection layer between the encoding and decoding layers to effectively combine the deep features with the shallow features， provide the location information of the hippocampus for subsequent segmentation， and improve the segmentation performance of the model. The experiment is carried out on ADNI dataset to verify the validity of the proposed model. The accuracy of the network model in the four evaluation indicators of IoU， DICE， accuracy and recall rate reaches 84.67%， 88.51%， 87.90% and 89.01% respectively. Compared with the existing advanced medical segmentation algorithm， the experimental results also show that the model has better segmentation ability and achieves better automatic segmentation effect of hippocampus image.

Key words: Keywords: Alzheimer’s disease, hippocampus segmentation, attention mechanism, feature fusion, atrous spatial pyramid pooling