Non-Local U-Nets for Biomedical Image Segmentation


  • Zhengyang Wang Texas A&M University
  • Na Zou Texas A&M University
  • Dinggang Shen University of North Carolina at Chapel Hill
  • Shuiwang Ji Texas A&M University



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.




How to Cite

Wang, Z., Zou, N., Shen, D., & Ji, S. (2020). Non-Local U-Nets for Biomedical Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6315-6322.



AAAI Technical Track: Machine Learning