# MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data

arXiv: https://arxiv.org/abs/2210.13642 · HTML: https://mlsanity.com/paper/2210.13642/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [Superhuman Accuracy on the SNEMI3D Connectomics Challenge](https://mlsanity.com/paper/1706.00120/) (2017)
- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions](https://mlsanity.com/paper/1902.05655/) (2019)
- [Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection](https://mlsanity.com/paper/2101.08418/) (2021)
- [MISeval: a Metric Library for Medical Image Segmentation Evaluation](https://mlsanity.com/paper/2201.09395/) (2022)

## Built on by (most cited first, 2 of 2)

- [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024)
- [FSS-UBrain: Multi-region Few-Shot Brain Tumor MRI Segmentation](https://mlsanity.com/paper/2609.32273/) (2026)

## Abstract (arXiv)

Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses when it comes to assessing certain edge cases. These limitations arouse when images with a very small region of interest or without a region of interest at all are assessed. As a solution for these limitations, we propose a new medical image segmentation metric: MISm. To evaluate MISm, the popular metrics in the medical image segmentation and MISm were compared using images of magnet resonance tomography from several scenarios. In order to allow application in the community and reproducibility of experimental results, we included MISm in the publicly available evaluation framework MISeval: https://github.com/frankkramer-lab/miseval/tree/master/miseval