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

TL;DR

This paper introduces MISm, a new medical image segmentation metric designed to better evaluate algorithms, especially in challenging cases with small or no regions of interest, and demonstrates its effectiveness through comparison with existing metrics.

Contribution

The paper presents MISm, a novel segmentation metric that addresses limitations of current measures in edge cases, and integrates it into a publicly available evaluation framework.

Findings

MISm outperforms existing metrics in edge cases

MISm is incorporated into MISeval for community use

Experimental results validate MISm's robustness

Abstract

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