Abstract
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.
Highlights
A SMALL archi-cortical brain structure, that manages short-term anecdotal and critical memory while depositing it into the long term memory, is known as hippocampus
OUR CONTRIBUTIONS we focus on hippocampi segmentation of brain MRIs, where our developed deep learning network has been derived from the famous U-Net architecture proposed by Ronneberger et al [19]
The results show that mean dice score (Dice) scores vary with both, the number of training subjects as well for left and right hippocampus regions respectively
Summary
A SMALL archi-cortical brain structure, that manages short-term anecdotal and critical memory while depositing it into the long term memory, is known as hippocampus. The hippocampus region is responsible for vocalbased and musical emotions as it forms a part of the temporal limbic system. In plain interpretation of voices and musical emotions, the amygdala is involved and enables the hippocampus to process even more complex information. It contributes towards decoding heterogeneous emotions related to music, thereby creating an alliance between memory and contextual information [1]. The human brain consists of two hippocampi regionsshaped like seahorses- commonly termed as left- and righthippocampi. They are termed as anterior and posterior hippocampus sub-fields. The region shows very little contrast on structural magnetic res-
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