Edge-Aware Network for Kidneys and Kidney Tumor Semantic Segmentation

Authors
Myronenko, Andriy
Hatamizadeh, Ali

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
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 entailing an encoder-decoder that specifically accounts for organ/tumor edge information by devising a dedicated network edge branch and edge-aware loss terms. We have evaluated our model on 2019 MICCAI KiTS Kidney Tumor Segmentation challenge dataset. Based on our own data split, we achieved 0.970 dice for kidney & tumor, and 0.834 dice for tumor segmentation.

Identifiers
doi: 10.24926/548719.009