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
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

Pagination

33-37

Auteurs

Dominik Müller (D)

IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany.
Medical Data Integration Center, Institute for Digital Medicine, University Hospital Augsburg, Germany.

Dennis Hartmann (D)

IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany.

Philip Meyer (P)

IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany.
Medical Data Integration Center, Institute for Digital Medicine, University Hospital Augsburg, Germany.

Florian Auer (F)

IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany.

Iñaki Soto-Rey (I)

Medical Data Integration Center, Institute for Digital Medicine, University Hospital Augsburg, Germany.

Frank Kramer (F)

IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany.

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Classifications MeSH