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Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning by Lei, Yang; Ding, Yifu; Qiu, Richard L. J.; Wang, Tonghe; Roper, Justin; Fu, Yabo; Shu, Hui-Kuo; Mao, Hui; Yang, Xiaofeng is a scholarly article available to read on EtoBox.

What is Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning about?

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

Author

Lei, Yang; Ding, Yifu; Qiu, Richard L. J.; Wang, Tonghe; Roper, Justin; Fu, Yabo; Shu, Hui-Kuo; Mao, Hui; Yang, Xiaofeng

Published

2023

Language

EN