Brain tumor segmentation is a fundamental step in surgical treatment and therapy.
Many hand-crafted and learning based methods have been proposed for automatic brain
tumor segmentation from MRI. Studies have shown that these approaches have their inherent
advantages and limitations. This work proposes a semantic label fusion algorithm by
combining two representative state-of-the-art segmentation algorithms: texture based
hand-crafted, and deep learning based methods to obtain robust tumor segmentation.
We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation
challenge dataset. The results show that the proposed method offers improved segmentation
by alleviating inherent weaknesses: extensive false positives in texture based method,
and the false tumor tissue classification problem in deep learning method, respectively.
Furthermore, we investigate the effect of patient's gender on the segmentation performance
using a subset of validation dataset. Note the substantial improvement in brain tumor
segmentation performance proposed in this work has recently enabled us to secure the
first place by our group in overall patient survival prediction task at the BRATS
2017 challenge.
