Non-Local U-Nets for Biomedical Image Segmentation Conference Paper uri icon

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.

published proceedings

  • Proceedings of the AAAI Conference on Artificial Intelligence

author list (cited authors)

  • Wang, Z., Zou, N. a., Shen, D., & Ji, S.

complete list of authors

  • Wang, Zhengyang||Zou, Na||Shen, Dinggang||Ji, Shuiwang

publication date

  • January 1, 2020 11:11 AM