Background Accurate segmentation of hippocampal subfields from magnetic resonance (MR) brain images is an important step for studying brain disorders, including epilepsy, Alzheimer’s Disease (AD) and Parkinson’s disease. However, it is a difficult task because of the low signal contrast and small structural size.
Methods Many advanced convolutional networks have been proposed and have achieved state-of-the-art performances in various applications. To take advantage of these advanced convolutional networks, in this paper, we propose a learning based ensemble strategy to integrate the results of different convolutional networks for hippocampus subfield segmentation. Our ensemble strategy is implemented by using a convolutional network. We have validated the proposed method based on a publicly available dataset.
Results The experiment results have showed that the proposed ensemble strategy can significantly improve the performance of each single convolutional network, and outperform the state-of-the-art hippocampus subfield segmentation method.
Conclusion
The proposed ensemble strategy is effective for combining multiple different convolutional networks in hippocampus subfield segmentation.
Background Hippocampus is a bilateral brain structure, involved in many brain disorders, such as epilepsy, Alzheimer’s disease (AD), and Parkinson's disease 1. It consists of several histologically and functionally specialized subfields: the subiculum (SUB), the cornu ammonis sectors (CA) 1–3, and the dentate gyrus (DG) 2. The studies have shown that different diseases affect different subfields, which suggest that hippocampal subfields may provide more precise information for earlier disease diagnosis than using the whole hippocampus 3.