  The brain tumor segmentation task aims to classify tissue into the whole
tumor (WT), tumor core (TC), and enhancing tumor (ET) classes using multimodel
MRI images. Quantitative analysis of brain tumors is critical for clinical
decision making. While manual segmentation is tedious, time-consuming, and
subjective, this task is at the same time very challenging to automatic
segmentation methods. Thanks to the powerful learning ability, convolutional
neural networks (CNNs), mainly fully convolutional networks, have shown
promising brain tumor segmentation. This paper further boosts the performance
of brain tumor segmentation by proposing hyperdense inception 3D UNet (HI-Net),
which captures multi-scale information by stacking factorization of 3D weighted
convolutional layers in the residual inception block. We use hyper dense
connections among factorized convolutional layers to extract more contexual
information, with the help of features reusability. We use a dice loss function
to cope with class imbalances. We validate the proposed architecture on the
multi-modal brain tumor segmentation challenges (BRATS) 2020 testing dataset.
Preliminary results on the BRATS 2020 testing set show that achieved by our
proposed approach, the dice (DSC) scores of ET, WT, and TC are 0.79457,
0.87494, and 0.83712, respectively.

Image and Video Processing;Computer Vision and Pattern Recognition