mlsanityData to 7 Oct 2026

MISeval: a Metric Library for Medical Image Segmentation Evaluation

arXiv 2201.09395 ↗First posted 23 Jan 2022cs.CV cs.LGcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 4 of its 12 references that are in the map. First posted on arXiv 23 Jan 2022.

Builds on

4 of 12 references.

References · 4

  1. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  2. 2019MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learningcited by 18
  3. 2020pymia: A Python package for data handling and evaluation in deep learning-based medical image analysiscited by 16
  4. 2021Rethinking Semantic Segmentation Evaluation for Explainability and Model Selectioncited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 2; 2025: 2; 2026: 1.

9 papers.

  1. 2022Towards a Guideline for Evaluation Metrics in Medical Image Segmentationcited by 58
  2. 2024Trans2Unet: Neural fusion for Nuclei Semantic Segmentationcited by 5
  3. 2025Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attentioncited by 4
  4. 2022MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Datacited by 2
  5. 2023Multi-Modal Evaluation Approach for Medical Image Segmentationcited by 1
  6. 2024Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRIcited by 1
  7. 2025Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusioncited by 1
  8. 2022Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Frameworkcited by 0
  9. 2026MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentationcited by 0