Non-local U-Net for Biomedical Image Segmentation

Code

• github.com/divelab/Non-local-U-Nets
  OfficialIn papertf★ 105
• github.com/zhengyang-wang/3D-Unet--Tensorflow
  tf★ 205
• github.com/Aykhan-sh/Non-local-U-Net-Pytorch
  pytorch★ 0

Abstract

Deep learning has shown its great promise in various biomedical image segmentation tasks. Existing models are typically based on U-Net and rely on an encoder-decoder architecture with stacked local operators to aggregate long-range information gradually. However, only using the local operators limits the efficiency and effectiveness. In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation. These blocks can be inserted into U-Net as size-preserving processes, as well as down-sampling and up-sampling layers. We perform thorough experiments on the 3D multimodality isointense infant brain MR image segmentation task to evaluate the non-local U-Nets. Results show that our proposed models achieve top performances with fewer parameters and faster computation.

Tasks

Reproductions