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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Bibliographic Details
Published in:Nature methods Vol. 21; no. 2; pp. 213 - 216
Main Authors: Hirling, Dominik, Tasnadi, Ervin, Caicedo, Juan, Caroprese, Maria V., Sjögren, Rickard, Aubreville, Marc, Koos, Krisztian, Horvath, Peter
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.02.2024
Nature Publishing Group
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-023-01942-8