  Background: The hippocampus plays a crucial role in memory and cognition.
Because of the associated toxicity from whole brain radiotherapy, more advanced
treatment planning techniques prioritize hippocampal avoidance, which depends
on an accurate segmentation of the small and complexly shaped hippocampus.
Purpose: To achieve accurate segmentation of the anterior and posterior regions
of the hippocampus from T1 weighted (T1w) MRI images, we developed a novel
model, Hippo-Net, which uses a mutually enhanced strategy. Methods: The
proposed model consists of two major parts: 1) a localization model is used to
detect the volume-of-interest (VOI) of hippocampus. 2) An end-to-end
morphological vision transformer network is used to perform substructures
segmentation within the hippocampus VOI. A total of 260 T1w MRI datasets were
used in this study. We conducted a five-fold cross-validation on the first 200
T1w MR images and then performed a hold-out test on the remaining 60 T1w MR
images with the model trained on the first 200 images. Results: In five-fold
cross-validation, the DSCs were 0.900+-0.029 and 0.886+-0.031for the
hippocampus proper and parts of the subiculum, respectively. The MSD were
0.426+-0.115mm and 0.401+-0.100 mm for the hippocampus proper and parts of the
subiculum, respectively. Conclusions: The proposed method showed great promise
in automatically delineating hippocampus substructures on T1w MRI images. It
may facilitate the current clinical workflow and reduce the physician effort.
