# Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation

arXiv: https://arxiv.org/abs/2111.00533 · HTML: https://mlsanity.com/paper/2111.00533/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 31 Oct 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning](https://mlsanity.com/paper/1910.09308/) (2019)
- [Normalization in Training U-Net for 2D Biomedical Semantic Segmentation](https://mlsanity.com/paper/1809.03783/) (2018)
- [Quantifying the unknown impact of segmentation uncertainty on image-based simulations](https://mlsanity.com/paper/2012.09913/) (2020)
- [Boundary-sensitive Network for Portrait Segmentation](https://mlsanity.com/paper/1712.08675/) (2017)

## Built on by (most cited first, 3 of 3)

- [Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs](https://mlsanity.com/paper/2206.06065/) (2022)
- [Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study](https://mlsanity.com/paper/2211.02475/) (2022)
- [Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data](https://mlsanity.com/paper/2605.12753/) (2026)

## Abstract (arXiv)

Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks. Due to considerable heterogeneity in shapes, colours and textures, demarcating object boundaries is particularly difficult in biomedical images, resulting in significant inter and intra-rater variability. Approaches, such as soft labelling and distance penalty term, apply a global transformation to the ground truth, redefining the loss function with respect to uncertainty. However, global operations are computationally expensive, and neither approach accurately reflects the uncertainty underlying manual annotation. In this paper, we propose the Boundary Uncertainty, which uses morphological operations to restrict soft labelling to object boundaries, providing an appropriate representation of uncertainty in ground truth labels, and may be adapted to enable robust model training where systematic manual segmentation errors are present. We incorporate Boundary Uncertainty with the Dice loss, achieving consistently improved performance across three well-validated biomedical imaging datasets compared to soft labelling and distance-weighted penalty. Boundary Uncertainty not only more accurately reflects the segmentation process, but it is also efficient, robust to segmentation errors and exhibits better generalisation.