# MISeval: a Metric Library for Medical Image Segmentation Evaluation

arXiv: https://arxiv.org/abs/2201.09395 · HTML: https://mlsanity.com/paper/2201.09395/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning](https://mlsanity.com/paper/1910.09308/) (2019)
- [pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis](https://mlsanity.com/paper/2010.03639/) (2020)
- [Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection](https://mlsanity.com/paper/2101.08418/) (2021)

## Built on by (most cited first, 9 of 9)

- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [Trans2Unet: Neural fusion for Nuclei Semantic Segmentation](https://mlsanity.com/paper/2407.17181/) (2024)
- [Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention](https://mlsanity.com/paper/2506.18335/) (2025)
- [MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data](https://mlsanity.com/paper/2210.13642/) (2022)
- [Multi-Modal Evaluation Approach for Medical Image Segmentation](https://mlsanity.com/paper/2302.04135/) (2023)
- [Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRI](https://mlsanity.com/paper/2412.00511/) (2024)
- [Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion](https://mlsanity.com/paper/2504.06158/) (2025)
- [Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Framework](https://mlsanity.com/paper/2206.08182/) (2022)
- [MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2607.00409/) (2026)

## Abstract (arXiv)

Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However, there is no universal metric library in Python for standardized and reproducible evaluation. Thus, we propose our open-source publicly available Python package MISeval: a metric library for Medical Image Segmentation Evaluation. The implemented metrics can be intuitively used and easily integrated into any performance assessment pipeline. The package utilizes modern CI/CD strategies to ensure functionality and stability. MISeval is available from PyPI (miseval) and GitHub: https://github.com/frankkramer-lab/miseval.