# Distance Map Loss Penalty Term for Semantic Segmentation

arXiv: https://arxiv.org/abs/1908.03679 · HTML: https://mlsanity.com/paper/1908.03679/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2019.

## Built on by (most cited first, 27 of 27)

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- [Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook](https://mlsanity.com/paper/2312.05391/) (2023)
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- [AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks](https://mlsanity.com/paper/2103.14026/) (2021)
- [Open-Set Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2405.19899/) (2024)
- [Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2107.12461/) (2021)
- [CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images](https://mlsanity.com/paper/2107.11920/) (2021)
- [Enhancing Boundary Segmentation for Topological Accuracy with Skeleton-based Methods](https://mlsanity.com/paper/2404.18539/) (2024)
- [Region-wise Loss for Biomedical Image Segmentation](https://mlsanity.com/paper/2108.01405/) (2021)
- [On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery](https://mlsanity.com/paper/2102.08312/) (2021)
- [Masked Supervised Learning for Semantic Segmentation](https://mlsanity.com/paper/2210.00923/) (2022)
- [SemSegLoss: A python package of loss functions for semantic segmentation](https://mlsanity.com/paper/2106.05844/) (2021)
- [Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training](https://mlsanity.com/paper/2202.06104/) (2022)
- [Zero Pixel Directional Boundary by Vector Transform](https://mlsanity.com/paper/2203.08795/) (2022)
- [Diabetic Retinopathy Lesion Segmentation through Attention Mechanisms](https://mlsanity.com/paper/2602.07301/) (2026)
- [Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021](https://mlsanity.com/paper/2208.03163/) (2022)
- [Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI](https://mlsanity.com/paper/2209.12232/) (2022)
- [A Dense Siamese U-Net trained with Edge Enhanced 3D IOU Loss for Image Co-segmentation](https://mlsanity.com/paper/2108.07491/) (2021)
- [Focal Attention Networks: optimising attention for biomedical image segmentation](https://mlsanity.com/paper/2111.00534/) (2021)
- [Effect of Prior-based Losses on Segmentation Performance: A Benchmark](https://mlsanity.com/paper/2201.02428/) (2022)
- [Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series](https://mlsanity.com/paper/2312.05148/) (2023)
- [Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network](https://mlsanity.com/paper/2411.14192/) (2024)
- [A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2501.00360/) (2024)
- [Adaptive Confidence-Wise Loss for Improved Lens Structure Segmentation in AS-OCT](https://mlsanity.com/paper/2508.08705/) (2025)
- [Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation](https://mlsanity.com/paper/2601.14338/) (2026)
- [From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation](https://mlsanity.com/paper/2604.14805/) (2026)
- [BASeg: Boundary-Aware Remote Sensing Segmentation with Structural Penalties](https://mlsanity.com/paper/2608.15683/) (2026)

## Abstract (arXiv)

Convolutional neural networks for semantic segmentation suffer from low performance at object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes is important for tracking of disease biomarkers such as tissue morphology and shape features. In this work, we propose a novel distance map derived loss penalty term for semantic segmentation. We propose to use distance maps, derived from ground truth masks, to create a penalty term, guiding the network's focus towards hard-to-segment boundary regions. We investigate the effects of this penalizing factor against cross-entropy, Dice, and focal loss, among others, evaluating performance on a 3D MRI bone segmentation task from the publicly available Osteoarthritis Initiative dataset. We observe a significant improvement in the quality of segmentation, with better shape preservation at bone boundaries and areas affected by partial volume. We ultimately aim to use our loss penalty term to improve the extraction of shape biomarkers and derive metrics to quantitatively evaluate the preservation of shape.