Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc. Automated accurate brain tissue segmentation like cerebro-spinal fluid (CSF), gray matter (GM), white matter (WM) is the basis of these studies and many researchers are seeking it to the best. Based on the truth that multi-channel segmentation network with its own ground truth achieves up to average dice ratio 0.98, we propose a novel method that we add a fourth channel with the ground truth of the most similar image's obtained by CBIR from the database. The results show that the method improves the segmentation performance, as measured by average dice ratio, by approximately 0.01 in the MRBrainS18 database. In addition, our method is concise and robust, which can be used to any network architecture that needs not be modified a lot.
@article{arxiv.1808.00457,
title = {A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation},
author = {Yao Sun and Yang Deng and Yue Xu and Shuo Zhang and Mingwang Zhu and Kehong Yuan},
journal= {arXiv preprint arXiv:1808.00457},
year = {2018}
}