Enhancement Mask for Hippocampus Detection and Segmentation

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 $0.897$ 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.