  Deep convolutional neural network (CNN) achieves remarkable performance for
medical image analysis. UNet is the primary source in the performance of 3D CNN
architectures for medical imaging tasks, including brain tumor segmentation.
The skip connection in the UNet architecture concatenates features from both
encoder and decoder paths to extract multi-contextual information from image
data. The multi-scaled features play an essential role in brain tumor
segmentation. However, the limited use of features can degrade the performance
of the UNet approach for segmentation. In this paper, we propose a modified
UNet architecture for brain tumor segmentation. In the proposed architecture,
we used densely connected blocks in both encoder and decoder paths to extract
multi-contextual information from the concept of feature reusability. In
addition, residual-inception blocks (RIB) are used to extract the local and
global information by merging features of different kernel sizes. We validate
the proposed architecture on the multi-modal brain tumor segmentation challenge
(BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT),
tumor core (TC), and enhancing tumor (ET) are 89.12%, 84.74%, and 79.12%,
respectively.
