# MeshMetrics: A Precise Implementation of Distance-Based Image Segmentation Metrics

arXiv: https://arxiv.org/abs/2509.05670 · HTML: https://mlsanity.com/paper/2509.05670/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 6 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation](https://mlsanity.com/paper/2212.04497/) (2022)
- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [Understanding metric-related pitfalls in image analysis validation](https://mlsanity.com/paper/2302.01790/) (2023)
- [Understanding implementation pitfalls of distance-based metrics for image segmentation](https://mlsanity.com/paper/2410.02630/) (2024)

## Abstract (arXiv)

The surge of research in image segmentation has yielded remarkable performance gains but also exposed a reproducibility crisis. A major contributor is performance evaluation, where both selection and implementation of metrics play critical roles. While recent efforts have improved the former, the reliability of metric implementation has received far less attention. Pitfalls in distance-based metric implementation can lead to considerable discrepancies between common open-source tools, for instance, exceeding 100 mm for the Hausdorff distance and 30%pt for the normalized surface distance for the same pair of segmentations. To address these pitfalls, we introduce MeshMetrics, a mesh-based framework that provides a more precise computation of distance-based metrics than conventional grid-based approaches. Through theoretical analysis and empirical validation, we demonstrate that MeshMetrics achieves higher accuracy and precision than established tools, and is substantially less affected by discretization artifacts, such as distance quantization. We release MeshMetrics as an open-source Python package, available at https://github.com/gasperpodobnik/MeshMetrics.