The kidney cancer is one of the most common cancer types. The treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we combine these two networks, and further boost the performance by mixup augmentation.
@article{arxiv.2208.04007,
title = {Ensembled Autoencoder Regularization for Multi-Structure Segmentation for Kidney Cancer Treatment},
author = {David Jozef Hresko and Marek Kurej and Jakub Gazda and Peter Drotar},
journal= {arXiv preprint arXiv:2208.04007},
year = {2022}
}