  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
