Abstract: Aiming at the problem that the deep U-net network is prone to gradient disappearance and low feature reuse rate, this paper proposes a 2D DenseU-net hippocampus segmentation algorithm framework, which combines DenseNet and U-net network structures. By constructing the dense connection between the current layer and all the previous layers in U-net, the problem of gradient disappearance of deep U-net is easily alleviated, and feature propagation and feature reuse are enhanced; DenseU-net maintains the advantages of dense connections while maintaining a long connection structure for downsampling and upsampling in U-net networks, helping to preserve shallow information. In addition, due to the small proportion, small size and unclear edge of the hippocampus, this paper adopts positive sample data enhancement technology, size cutting technique, deletion of invalid sample technology and edge sampling technique for the data samples, which effectively balances the positive and negative samples, and magnified the hippocampus details. These pre-processing techniques ensure that the complete features are acquired for network training. The experimental results on the public data set ADNI (Alzheimer's Disease Neuroimaging Initiative) show that the proposed method can achieve an average segmentation Dice accuracy of 92.63%, which is better than the traditional hippocampus segmentation method and some popular hippocampus segmentation models based on deep learning.

Key words: U-net, gradient disappearance, DenseU-net, dense connection, Hippocampus