  Accurate brain tumour segmentation is a crucial step towards improving
disease diagnosis and proper treatment planning. In this paper, we propose a
deep-learning based method to segment a brain tumour into its subregions: whole
tumour, tumour core and enhancing tumour. The proposed architecture is a 3D
convolutional neural network based on a variant of the U-Net architecture of
Ronneberger et al. [17] with three main modifications: (i) a heavy encoder,
light decoder structure using residual blocks (ii) employment of dense blocks
instead of skip connections, and (iii) utilization of self-ensembling in the
decoder part of the network. The network was trained and tested using two
different approaches: a multitask framework to segment all tumour subregions at
the same time and a three-stage cascaded framework to segment one sub-region at
a time. An ensemble of the results from both frameworks was also computed. To
address the class imbalance issue, appropriate patch extraction was employed in
a pre-processing step. The connected component analysis was utilized in the
post-processing step to reduce false positive predictions. Experimental results
on the BraTS20 validation dataset demonstrates that the proposed model achieved
average Dice Scores of 0.90, 0.82, and 0.78 for whole tumour, tumour core and
enhancing tumour respectively.
