  Automated segmentation of kidneys and kidney tumors is an important step in
quantifying the tumor's morphometrical details to monitor the progression of
the disease and accurately compare decisions regarding the kidney tumor
treatment. Manual delineation techniques are often tedious, error-prone and
require expert knowledge for creating unambiguous representation of kidneys and
kidney tumors segmentation. In this work, we propose an end-to-end boundary
aware fully Convolutional Neural Networks (CNNs) for reliable kidney and kidney
tumor semantic segmentation from arterial phase abdominal 3D CT scans. We
propose a segmentation network consisting of an encoder-decoder architecture
that specifically accounts for organ and tumor edge information by devising a
dedicated boundary branch supervised by edge-aware loss terms. We have
evaluated our model on 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge
dataset and our method has achieved dice scores of 0.9742 and 0.8103 for kidney
and tumor repetitively and an overall composite dice score of 0.8923.
