Authors
Li, Lei
Lian, Sheng
Luo, Zhiming
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Issue Date
2019
Publisher
University of Minnesota Libraries Publishing
Type
Article
Abstract
Accurate segmentation of kidney tumor in CT images is a challenging task. For solving this, we proposed SE-ResNeXT U-Net (SERU) model, which combines the advantages of SE-Net, ResNeXT and U-Net. For utilizing context information and key slices’ information, we implement our model in a coarse-to-fine manner. We find left and right kidney’s key slice respectively, and obtain key patches for refine training. We train and test our method on the KiTS19 Challenge. The predictions on kidney segmentation and tumor segmentation by our model show promising results.
Identifiers
doi: 10.24926/548719.022