Abstract:
Even with proper acquisition of brain tumor images, the accurate and reliable segmentation of tumors in
brain is a complicated job. Automatic segmentation become possible with development of deep learning algorithms that brings plethora of solutions in this research prospect. In this paper, we designed
a network architecture named as residual cyclic unpaired encoder-decoder network (RescueNet) using
residual and mirroring principles. RescueNet uses unpaired adversarial training to segment the whole
tumor followed by core and enhance regions in a brain MRI scan. The problem in automatic brain tumor
analysis is preparing large scale labeled data for training of deep networks which is a time consuming
and tedious task. To eliminate this need of paired data we used unpaired training approach to train the
proposed network. Performance evaluation parameters are taken as DICE and Sensitivity measure. The
experimental results are tested on BraTS 2015 and BraTS 2017 [1] dataset and the result outperforms the
existing methods for brain tumor segmentation. The combination of domain-specific segmentation methods and general-purpose adversarial learning loomed to leverage huge advantages for medical imaging
applications and can improve the ability of automated algorithms to assist radiologists.
