Segmentation metric misinterpretations in bioimage analysis

Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algo...

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Vydané v:Nature methods Ročník 21; číslo 2; s. 213 - 216
Hlavní autori: Hirling, Dominik, Tasnadi, Ervin, Caicedo, Juan, Caroprese, Maria V., Sjögren, Rickard, Aubreville, Marc, Koos, Krisztian, Horvath, Peter
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Nature Publishing Group US 01.02.2024
Nature Publishing Group
Predmet:
631/114/1305
631/114/1564
631/1647/328
Algorithms
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Brief Communication
Data science
Datasets
Image analysis
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Life Sciences
Object recognition
Proteomics
ISSN:1548-7091, 1548-7105, 1548-7105
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Popis
Shrnutí:Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled. This study shows the importance of proper metrics for comparing algorithms for bioimage segmentation and object detection by exploring the impact of metrics on the relative performance of algorithms in three image analysis competitions.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-023-01942-8