  Gliomas are one of the most frequent brain tumors and are classified into
high grade and low grade gliomas. The segmentation of various regions such as
tumor core, enhancing tumor etc. plays an important role in determining
severity and prognosis. Here, we have developed a multi-threshold model based
on attention U-Net for identification of various regions of the tumor in
magnetic resonance imaging (MRI). We propose a multi-path segmentation and
built three separate models for the different regions of interest. The proposed
model achieved mean Dice Coefficient of 0.59, 0.72, and 0.61 for enhancing
tumor, whole tumor and tumor core respectively on the training dataset. The
same model gave mean Dice Coefficient of 0.57, 0.73, and 0.61 on the validation
dataset and 0.59, 0.72, and 0.57 on the test dataset.
