The assessment of small bowel motility with attentive deformable neural network
作者: Xing WuMingyu ZhongYike GuoHamido Fujita
作者单位: 1School of Computer Engineering and Science, Shanghai University, Shanghai, China
2Shanghai Institute for Advanced Communication and Data Science, Shanghai, China
3Department of Computing, Imperial College London, London, United Kingdom
4Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam
5Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, Japan
刊名: Information Sciences, 2020, Vol.508 , pp.22-32
来源数据库: Elsevier Journal
DOI: 10.1016/j.ins.2019.08.059
关键词: Small bowel motilityCine-MRIRecursive neural networksAttentive encoder–decoderDeformable convolution
原始语种摘要: Abstract(#br)The small bowel is the longest part of the gastrointestinal tract and quick assessment of its motility using Cine-MRI is conducive to the diagnosis of gastroenteric diseases. Because of the complex shape changes that occur frequently in the small bowel, approaches involving human designed features and simple convolutional neural network (CNN) methods fail to achieve satisfactory performance on massive datasets. To meet the challenge of assessing small bowel motility automatically, we propose the integration of deformable convolutional networks into attentive encoder–decoder. With the help of deformable convolution, a tailored CNN can track small bowel segments in different shapes from each MR image of a Cine-MRI sequence. The proposed attentive encoder–decoder performed...
全文获取路径: Elsevier  (合作)
影响因子:3.643 (2012)

  • deformable 可变形的
  • convolution 褶积
  • neural 神经系统的
  • bowel 
  • attentive 注意的
  • motility 游动性
  • small 小的
  • automatically 自动地
  • propose 提议
  • network 网络