3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks
This work addresses the problem of tedious and error-prone manual segmentation for medical professionals, though it is incremental as it builds on existing CNN methods with boundary-aware enhancements.
The paper tackles automated segmentation of kidneys and kidney tumors from 3D CT scans to improve disease monitoring and treatment decisions, achieving dice scores of 0.9742 for kidneys and 0.8103 for tumors.
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.