  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.
