Authors
Su, Chengwei
Du, Bo

Abstract
There are more than 400,000 new cases of kidney cancer each year [1], and surgery is its most common treatment [2]. Due to the wide variety in kidney and kidney tumor morphology, there is currently great interest in how tumor morphology relates to surgical outcomes, [3,4] as well as in developing advanced surgical planning techniques [5]. Automatic semantic segmentation is a promising tool for these efforts, but morphological heterogeneity makes it a difficult problem. In this paper, we use ResUNet to solve this problem. The ResUNet combines the UNet with residual connection, which is fast and has less parameters. The source code can be found at: https://github.com/FlyGlider/Kits19

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