Authors
Paolucci, Iwan
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Issue Date
2019
Publisher
University of Minnesota Libraries Publishing
Type
Article
Abstract
Each year, there are about 400’000 new cases of kidney cancer worldwide causing around 175’000 deaths. For clinical decision making it is important to understand the morphometry of the tumor, which involves the timeconsuming task of delineating tumor and kidney in 3D CT images. Automatic segmentation could be an important tool for clinicians and researchers to also study the correlations between tumor morphometry and clinical outcomes. We present a segmentation method which combines the popular U-Net convolutional neural network architecture with post-processing based on statistical constraints of the available training data. The full implementation, based on PyTorch, and the trained weights can be found on GitHub http://github.com/ipa/kits2019.
Identifiers
doi: 10.24926/548719.088