Authors
Mu, Guangrui
Lin, Zhiyong
Han, Miaofei
Yao, Guang
Gao, Yaozong

Abstract
Accurate segmentation of kidney tumors can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. In this work, we propose multi-resolution 3D V-Net networks to automatically segment kidney and renal tumor in computed tomography (CT) images. Specifically, we adopt two resolutions and propose a customized V-Net model called VB-Net for both resolutions. The VB-Net model in the coarse resolution can robustly localize the organs, while the VB-Net model in the fine resolution can accurately refine the boundary of each organ or lesion. We experiment in the KiTS19 challenge, which shows promising performance.

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