  In this paper we propose a 2D deep residual Unet with 104 convolutional
layers (DR-Unet104) for lesion segmentation in brain MRIs. We make multiple
additions to the Unet architecture, including adding the 'bottleneck' residual
block to the Unet encoder and adding dropout after each convolution block
stack. We verified the effect of introducing the regularisation of dropout with
small rate (e.g. 0.2) on the architecture, and found a dropout of 0.2 improved
the overall performance compared to no dropout, or a dropout of 0.5. We
evaluated the proposed architecture as part of the Multimodal Brain Tumor
Segmentation (BraTS) 2020 Challenge and compared our method to DeepLabV3+ with
a ResNet-V2-152 backbone. We found that the DR-Unet104 achieved a mean dice
score coefficient of 0.8862, 0.6756 and 0.6721 for validation data, whole
tumor, enhancing tumor and tumor core respectively, an overall improvement on
0.8770, 0.65242 and 0.68134 achieved by DeepLabV3+. Our method produced a final
mean DSC of 0.8673, 0.7514 and 0.7983 on whole tumor, enhancing tumor and tumor
core on the challenge's testing data. We produced a competitive lesion
segmentation architecture, despite only 2D convolutions, having the added
benefit that it can be used on lower power computers than a 3D architecture.
The source code and trained model for this work is openly available at
https://github.com/jordan-colman/DR-Unet104.
