SERU: A cascaded SE‐ResNeXT U‐Net for kidney and tumor segmentation. (23rd March 2020)

• Record Type: Journal Article
• Authors: Xie, Xiuzhen; Li, Lei; Lian, Sheng; Chen, Shaohao; Luo, Zhiming
• Is Part Of: Concurrency and computation. Volume 32:Number 14(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-23
• Subjects: kidney -- SE‐ResNeXT -- segmentation -- tumor -- U‐Net; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5738
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: According to statistics, kidney cancer is one of the most deadly cancer. An early and accurate diagnosis can significantly increase the cure rate. Accurate segmentation of kidney tumors in CT images plays an important role in kidney cancer diagnosis. However, it is a challenging task due to many different aspects, such as low contrast, irregular motion, diverse shapes, and sizes. For solving this issue, we proposed a SE‐R esNeXT U ‐Net (SERU) model in this study, which takes the advantages of SE‐Net, ResNeXT and U‐Net. Besides, we implement our model in a coarse‐to‐fine manner to utilize the information of context and key slices from the left and right kidney. We train and test our method on the KiTS19 Challenge. Experimental results demonstrate that our model can achieve promising results.