Enhancement Mask for Hippocampus Detection and Segmentation
This work addresses a challenging medical imaging problem for brain analysis, with incremental improvements in segmentation accuracy and training efficiency.
The paper tackles the problem of detecting and segmenting hippocampal structures in volumetric brain images by proposing a two-stage 3D fully convolutional neural network that uses an enhancement mask for fine segmentation, achieving mean Dice Similarity Coefficients of 0.897 and 0.900 for the left and right hippocampus, respectively.
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.