Authors
Daza, Laura
Gómez, Catalina
Arbeláez, Pablo
Issue Date
2019
Publisher
University of Minnesota Libraries Publishing
Type
Article
Abstract
We present a fully automatic method for segmentation of kidney tumors in CT volumetric data based on DeepLab v3+, the stateof-the-art model in semantic segmentation in natural images. We adapt the architecture to process medical data and reduce the computational complexity to allow training 3D models. We evaluate our approach on the Kidney Tumor Segmentation Challenge 2019 dataset, and define a validation set to experiment with the model’s parameters. In our validation set, we report a dice score of XX for the kidney class and YY for the tumor class.
Identifiers
doi: 10.24926/548719.077