  In the last decade, research on artificial intelligence has seen rapid growth
with deep learning models, especially in the field of medical image
segmentation. Various studies demonstrated that these models have powerful
prediction capabilities and achieved similar results as clinicians. However,
recent studies revealed that the evaluation in image segmentation studies lacks
reliable model performance assessment and showed statistical bias by incorrect
metric implementation or usage. Thus, this work provides an overview and
interpretation guide on the following metrics for medical image segmentation
evaluation in binary as well as multi-class problems: Dice similarity
coefficient, Jaccard, Sensitivity, Specificity, Rand index, ROC curves, Cohen's
Kappa, and Hausdorff distance. As a summary, we propose a guideline for
standardized medical image segmentation evaluation to improve evaluation
quality, reproducibility, and comparability in the research field.
