SERU: A cascaded SE-ResNeXT U-Net for kidney and tumor segmentation

被引：21
|
作者：
Xie, Xiuzhen [1 ]
Li, Lei [2 ]
Lian, Sheng [2 ]
Chen, Shaohao [4 ]
Luo, Zhiming [3 ]
机构：
[1] Longyan Univ, Coll Math & Informat Engn, Longyan, Peoples R China
[2] Xiamen Univ, Artificial Intelligence Dept, Xiamen, Peoples R China
[3] Xiamen Univ, Postdoctoral Mobile Stn Informat & Commun Engn, Xiamen 361005, Fujian, Peoples R China
[4] Fujian Med Univ, Affiliated Hosp 1, Fuzhou, Peoples R China
来源：
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE | 2020年 / 32卷 / 14期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
kidney; SE-ResNeXT; segmentation; tumor; U-Net;
D O I：
10.1002/cpe.5738
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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 aSE-ResNeXTU-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.
引用
收藏
页数：11
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