When seeing is not believing: application-appropriate validation matters for quantitative bioimage analysis

A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and d...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Nature methods Jg. 20; H. 7; S. 968 - 970
Hauptverfasser: Chen, Jianxu, Viana, Matheus P., Rafelski, Susanne M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Nature Publishing Group US 01.07.2023
Nature Publishing Group
Schlagworte:
631/114/1564
631/80/2373
Accuracy
Algorithms
Automation
Bioinformatics
Biological Microscopy
Biological Techniques
Biology
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Cell cycle
Comment
Datasets
Deep learning
Graduate students
Image analysis
Image processing
Life Sciences
Microscopy
Mitochondria
Morphology
Proteomics
Streamlining
ISSN:1548-7091, 1548-7105, 1548-7105
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and discuss possible solutions for automating and streamlining the validation process in the next five to ten years.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-023-01881-4