Enhancement Mask for Hippocampus Detection and Segmentation
Dengsheng Chen, Wenxi Liu, You Huang, Tong Tong, Yuanlong Yu

TL;DR
This paper introduces a two-stage 3D CNN approach for hippocampus detection and segmentation, utilizing an enhancement mask to improve accuracy and training efficiency in volumetric brain imaging.
Contribution
The novel enhancement mask layer significantly improves hippocampus segmentation accuracy and accelerates training in a two-stage 3D CNN framework.
Findings
Achieved DSC of 0.897 and 0.900 for left and right hippocampus.
Enhanced training speed and segmentation accuracy with the proposed mask.
Outperformed state-of-the-art methods on a public dataset.
Abstract
Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging. In this paper, we propose a two-stage 3D fully convolutional neural network that efficiently detects and segments the hippocampal structures. In particular, our approach first localizes the hippocampus from the whole volumetric image while obtaining a proposal for a rough segmentation. After localization, we apply the proposal as an enhancement mask to extract the fine structure of the hippocampus. The proposed method has been evaluated on a public dataset and compares with state-of-the-art approaches. Results indicate the effectiveness of the proposed method, which yields mean Dice Similarity Coefficients (i.e. DSC) of and for the left and right hippocampus, respectively. Furthermore, extensive experiments manifest that the…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Brain Tumor Detection and Classification · Advanced Image and Video Retrieval Techniques
