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Background: The hippocampus plays a crucial role in memory and cognition.Because of the associated toxicity from whole brain radiotherapy, more advancedtreatment planning techniques prioritize hippocampal avoidance, which dependson an accurate segmentation of the small and complexly shaped hippocampus.Purpose: To achieve accurate segmentation of the anterior and posterior regionsof the hippocampus from T1 weighted (T1w) MRI images, we developed a novelmodel, Hippo-Net, which uses a mutually enhanced strategy. Methods: Theproposed model consists of two major parts: 1) a localization model is used todetect the volume-of-interest (VOI) of hippocampus. 2) An end-to-endmorphological vision transformer network is used to perform substructuressegmentation within the hippocampus VOI. A total of 260 T1w MRI datasets wereused in this study. We conducted a five-fold cross-validation on the first 200T1w MR images and then performed a hold-out test on the remaining 60 T1w MRimages with the model trained on the first 200 images. Results: In five-foldcross-validation, the DSCs were 0.900+-0.029 and 0.886+-0.031for thehippocampus proper and parts of the subiculum, respectively. The MSD were0.426+-0.115mm and 0.401+-0.100 mm for the hippocampus proper and parts of thesubiculum, respectively. Conclusions: The proposed method showed great promisein automatically delineating hippocampus substructures on T1w MRI images. Itmay facilitate the current clinical workflow and reduce the physician effort.

Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning