A Modified U-Net Based Framework for Automated Segmentation of Hippocampus Region in Brain MRI

An accurate localization of the brain anatomical structure for correct and reliable diagnostic strategies is of great concern in many bio-medical applications. Towards this end, manual or semi-automated delineation methods used are found to be time consuming. Herein, to address this problem, we present an enhanced model for automated segmentation of two neighboring small structures of the brain in the Hippocampus region i.e., anterior and posterior. Our aim is to improve the segmentation performance, where the proposed architecture captures contextual information in encoding path and enables precise localization by utilizing the decoding path in a symmetric way. In particular, our proposed methodology enhances the original U-Net architecture with 3-dimensional (3D) data processing and employs spatial elastic deformation. Further, we evaluated the segmentation performance using recursive U-Net for comparison. The effectiveness of different optimization strategies are evaluated on a publicly available data comprising of 3D magnetic resonance imaging volumes from mono-modal hippocampus region. Our experimental results demonstrate the robustness of the proposed model by using patch-based augmentation technique for hippocampal segmentation.