MISeval: A Metric Library for Medical Image Segmentation Evaluation.
Biomedical image segmentation
Evaluation
Medical Image Analysis
Open-source framework
Performance assessment
Reproducibility
Journal
Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582
Informations de publication
Date de publication:
25 May 2022
25 May 2022
Historique:
entrez:
25
5
2022
pubmed:
26
5
2022
medline:
27
5
2022
Statut:
ppublish
Résumé
Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However, there is no universal metric library in Python for standardized and reproducible evaluation. Thus, we propose our open-source publicly available Python package MISeval: a metric library for Medical Image Segmentation Evaluation. The implemented metrics can be intuitively used and easily integrated into any performance assessment pipeline. The package utilizes modern DevOps strategies to ensure functionality and stability. MISeval is available from PyPI (miseval) and GitHub: https://github.com/frankkramer-lab/miseval.
Identifiants
pubmed: 35612011
pii: SHTI220391
doi: 10.3233/SHTI220391
doi:
Types de publication
Journal Article
Langues
eng