A 3D Multi-Scale Multi-Attention UNet for Automatic Hippocampal Segmentation

Hippocampus is one of the first affected brain regions in Alzheimer's disease (AD). To help AD diagnosis, hippocampal atrophy have been studied extensively, but accurate segmentation of hippocampus has always been a difficult problem. In this study, we propose a 3D multi-scale multi-attention UNet with the use of multi-scale inception module, residual connections and attention modules for automatic hippocampal segmentation. Experimental results on the HarP dataset show that our proposed method can achieve average Dice coefficient of 0.827 for hippocampal segmentation, which outperform classic 3D UNet. The efficiency and accuracy of the proposed methods suggests that it may facilitate the study of the hippocampus for large-scale studies.