Abstract:
Studies have found out that tumors in brain are one of the fiercest diseases which can ultimately
lead to death. Gliomas are the most commonly found primary tumors that are very hard to predict
and can be found anywhere in the brain. It is prime objective to differentiate the different tumor
tissues such as enhancing tissues, edema, from healthy ones. To do this task, two types of
segmentation techniques come into existent i.e. manual and automatic. The automation methods
of brain tumor segmentation have gained ground over manual segmentation algorithms and further
its estimation is very closer to clinical results. In this paper we propose a comprehensive U-NET
architecture with modification in their layers for 2D slices segmentation as a major contribution to
BRATS 2015 challenge.. Then we enlisted different datasets that are available publicly i.e. BRATS
and DICOM. Further, we present a robust framework inspired from U-NET model with addition
and modification of layers and image pre-processing methodology such as contrast enhancement
for visible input and output details. In this way our approach achieves highest dice score 0.92 on
the publicly available BRATS 2015 dataset and with better time constraint i.e. training time
decreases to 80-90 minute instead of previously 2 to 3 days. We put our approach to the test on the
benchmark brats 2015 dataset, and it outperformed the competition in terms of performance and
Dice Score.
