Using Two-stage Network to Segment Kidneys and Kidney Tumors

Authors
Chen, Pan
Xu, Chenghai
He, Jie
Sun, Chengwei
Ma, Yingying
Sun, Fenglong

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
There are many new cases of kidney cancer each year, and surgery is the most common treatment. To assist doctors in surgical planning, an accurate and automatic kidney and tumor segmentation method is helpful in the clinical practice. In this paper, we propose a deep learning framework for the segmentation of kidneys and tumors in abdominal CT images. The key idea is using a two-stage strategy. First, for each case, we use a 3d U-shape convolution network to get the localization of each kidney. Then using next 3d U-shape convolution network we obtain the precise segmentation results of each kidney. Finally, merge the results to obtain the complete segmentation. Also, we try some tricks to improve the performance.

Identifiers
doi: 10.24926/548719.049