Two-phase Framework for Automatic Kidney and Kidney Tumor Segmentation

Authors
Wei, Hao
Wang, Qin
Zhao, Weibing
Zhang, Minqing
Yuan, Kun
Li, Zhen

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Precise segmentation of kidney and kidney tumor is essential for computer aided diagnosis. Considering the diverse shape, low contrast with surrounding tissues and small tumor volumes, it’s still challenging to segment kidney and kidney tumor accurately. To solve this problem, we proposed a two-phase framework for automatic segmentation of kidney and kidney tumor. In the first phase, the approximate localization of kidney and kidney tumor is predicted by a coarse segmentation network to overcome GPU memory limitation. Taking the coarse prediction from first phase as input, the region of kidney and tumor is cropped and trained in an enhanced two-stage network to achieve a fine-grained segmentation result in the second phase. Besides, our network prediction flows into multiple post-processing steps to remove false positive such as cyst by taking medical prior knowledge into consideration. Data argumentation through registration and ensemble models are used to further enhance performance.

Identifiers
doi: 10.24926/548719.043