# Uncertainty Driven Bottleneck Attention U-net for Organ at Risk Segmentation

arXiv: https://arxiv.org/abs/2303.10796 · HTML: https://mlsanity.com/paper/2303.10796/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Multi-scale self-guided attention for medical image segmentation](https://mlsanity.com/paper/1906.02849/) (2019)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)
- [ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation](https://mlsanity.com/paper/1907.10936/) (2019)
- [Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net](https://mlsanity.com/paper/1809.00960/) (2018)
- [Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention](https://mlsanity.com/paper/2110.12372/) (2021)

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

- [An Efficient Dual-Line Decoder Network with Multi-Scale Convolutional Attention for Multi-organ Segmentation](https://mlsanity.com/paper/2508.17007/) (2025)

## Abstract (arXiv)

Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for automated segmentation methods and can be crucial for downstream radiation treatment planning. U-net has become a de-facto standard for medical image segmentation and is frequently used as a common baseline in medical image segmentation tasks. In this paper, we propose a multiple decoder U-net architecture and use the segmentation disagreement between the decoders as attention to the bottleneck of the network for segmentation refinement. While feature correlation is considered as attention in most cases, in our case it is the uncertainty from the network used as attention. For accurate segmentation, we also proposed a CT intensity integrated regularization loss. Proposed regularisation helps model understand the intensity distribution of low contrast tissues. We tested our model on two publicly available OAR challenge datasets. We also conducted the ablation on each datasets with the proposed attention module and regularization loss. Experimental results demonstrate a clear accuracy improvement on both datasets.