  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.
