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Enhancement Mask for Hippocampus Detection and Segmentation
- Source :
- 2018 IEEE International Conference on Information and Automation (ICIA)
- Publication Year :
- 2019
-
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 $0.897$ and $0.900$ for the left and right hippocampus, respectively. Furthermore, extensive experiments manifest that the proposed enhancement mask layer has remarkable benefits for accelerating training process and obtaining more accurate segmentation results.<br />Comment: This paper has been published in the proceedings of IEEE International Conference on Information and Automation 2018
- Subjects :
- Computer Science - Computer Vision and Pattern Recognition
Subjects
Details
- Database :
- arXiv
- Journal :
- 2018 IEEE International Conference on Information and Automation (ICIA)
- Publication Type :
- Report
- Accession number :
- edsarx.1902.04244
- Document Type :
- Working Paper