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Performance measures are an important tool for assessing and comparingdifferent medical image segmentation algorithms. Unfortunately, the currentmeasures have their weaknesses when it comes to assessing certain edge cases.These limitations arouse when images with a very small region of interest orwithout a region of interest at all are assessed. As a solution for theselimitations, we propose a new medical image segmentation metric: MISm. Toevaluate MISm, the popular metrics in the medical image segmentation and MISmwere compared using images of magnet resonance tomography from severalscenarios. In order to allow application in the community and reproducibilityof experimental results, we included MISm in the publicly available evaluationframework MISeval:https://github.com/frankkramer-lab/miseval/tree/master/miseval

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