Automatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization
Pinyuan Zhong, Yue Zhang, Xiaoying Tang · 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · 2021
In this paper, we proposed and validated a fully automatic pipeline for hippocampal surface generation via 3D U-net coupled with active shape modeling (ASM). Principally, the proposed pipeline consisted of three steps. In the beginning, for each magnetic resonance image, a 3D U-net was employed to obtain the automatic hippocampus segmentation at each hemisphere. Secondly, ASM was performed on a group of pre-obtained template surfaces to generate mean shape and shape variation parameters through principal component analysis. Ultimately, hybrid particle swarm optimization was utilized to search for the optimal shape variation parameters that best match the segmentation. The hippocampal surface was then generated from the mean shape and the shape variation parameters. The proposed pipeline was observed to provide hippocampal surfaces at both hemispheres with high accuracy, correct anatomical topology, and sufficient smoothness.Clinical relevance-This work provides a useful tool for generating hippocampal surfaces which are important biomarkers for a variety of brain disorders.