MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data
This addresses limitations in segmentation evaluation for medical imaging, particularly for edge cases, but is incremental as it builds on existing metrics.
The authors tackled the problem of evaluating medical image segmentation algorithms when images have very small or no regions of interest, proposing a new metric called MISm. They compared MISm to popular metrics using MRI data and integrated it into a public framework for community use.
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