Towards a guideline for evaluation metrics in medical image segmentation

• Published in: BMC research notes Vol. 15; no. 1; pp. 210 - 8
• Main Authors: Müller, Dominik, Soto-Rey, Iñaki, Kramer, Frank
• Format: Journal Article
• Language: English
• Published: London BioMed Central 20.06.2022; BioMed Central Ltd; BMC
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Automation; Benchmarking; Bias; Biomedical and Life Sciences; Biomedical image segmentation; Semantic segmentation; Medical Image Analysis; Biomedicine; Commentary; Computer-aided medical diagnosis; Deep learning; Diagnostic imaging; Evaluation; Guideline; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Life Sciences; Medical imaging; Medicine/Public Health; Methods; Performance assessment; Practice guidelines (Medicine); Reproducibility; Reproducibility of Results; ROC Curve; Segmentation; Statistics; Germany; Biomedical image segmentation; Semantic segmentation; Medical Image Analysis; Evaluation; Guideline; Reproducibility; Performance assessment
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/s13104-022-06096-y

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. Furthermore, common issues like class imbalance and statistical as well as interpretation biases in evaluation are discussed. As a summary, we propose a guideline for standardized medical image segmentation evaluation to improve evaluation quality, reproducibility, and comparability in the research field.