# Non-local U-Net for Biomedical Image Segmentation

arXiv: https://arxiv.org/abs/1812.04103 · HTML: https://mlsanity.com/paper/1812.04103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Channel Boosted CNN-Transformer-based Multi-Level and Multi-Scale Nuclei Segmentation](https://mlsanity.com/paper/2407.19186/) (2024)

## Abstract (arXiv)

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.