Brain Tumour Segmentation Using Probabilistic U-Net

Chinmay Savadikar, Rahul Kulhalli, Bhushan Garware

Abstract: We describe our approach towards the segmentation task of the BRATS 2020 challenge. We use the Probabilistic UNet to explore the effect of sampling different segmentation maps, which may be useful to experts when the opinions of different experts vary. We use 2D segmentation models and approach the problem in a slice-by-slice manner. To explore the possibility of designing robust models, we use self attention in the UNet, and the prior and posterior networks, and explore the effect of varying the number of attention blocks on the quality of the segmentation. Our model achieves Dice scores of 0.81898 on Whole Tumour, 0.71681 on Tumour Core, and 0.68893 on Enhancing Tumour on the Validation data, and 0.7988 on Whole Tumour, 0.7771 on Tumour Core, and 0.7249 on Enhancing Tumour on the Testing data. Our code is available at https://github.com/rahulkulhalli/BRATS2020 .