Authors
Yu, Pengxin
Cui, Xing
Tian, Xi
Ma, Jiechao
Zhang, Rongguo
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Issue Date
2019
Publisher
University of Minnesota Libraries Publishing
Type
Article
Abstract
In this report, we present our method description of the submission to Kidney Tumor Segmentation Challenge 2019. In this challenge, the goal is to segment the kidney and kidney tumor from the CT scans. Our method is based on a common neural architecture U-Net variant, while we pay more attention to the preprocessing stage to better understand the kidney data and postprocessing stage to reduce false positives. The experiments and results show that our proposed methods increase the segmentation accuracy compared to the basic model.
Identifiers
doi: 10.24926/548719.036