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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Abstract	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.
AbstractList	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. 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. 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. 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.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.
Author	Horvath, Peter Hirling, Dominik Caicedo, Juan Sjögren, Rickard Koos, Krisztian Aubreville, Marc Tasnadi, Ervin Caroprese, Maria V.
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Cites_doi	10.1007/s11263-014-0733-5 10.1038/s41592-019-0612-7 10.1016/j.cels.2020.04.003 10.1109/MLSP55214.2022.9943312 10.1016/j.compbiomed.2022.105792 10.1038/s41592-020-01018-x 10.1109/ISBI48211.2021.9433928 10.1016/j.media.2022.102371 10.1038/s41598-020-61808-3 10.1016/j.media.2022.102523 10.1016/j.media.2022.102699 10.1016/j.imu.2020.100297 10.1109/CVPR.2016.90 10.1007/978-3-030-00934-2_30 10.1038/s41598-021-94217-1 10.1038/s41592-020-01008-z 10.1109/IWSSIP48289.2020.9145130 10.1109/ICCV.2017.322 10.1038/s41592-021-01249-6 10.1007/978-3-319-10602-1_48
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References_xml	– reference: CaicedoJCNucleus segmentation across imaging experiments: the 2018 Data Science BowlNat. Methods201916124712531:CAS:528:DC%2BC1MXitVSlsbrK10.1038/s41592-019-0612-7316364596919559. – reference: AubrevilleMMitosis domain generalization in histopathology images—the MIDOG challengeMed. Image Anal.20238410269910.1016/j.media.2022.10269936463832 – reference: Mabon, J., Ortner, M. & Zerubia, J. CNN-based energy learning for MPP object detection in satellite images. In 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP) pp. 1–6 (IEEE, 2022). – reference: LalitMTomancakPJugFEmbedSeg: embedding-based instance segmentation for biomedical microscopy dataMed. Image Anal.20228110252310.1016/j.media.2022.10252335926335 – reference: Lin, T. Y. et al. Microsoft COCO: common objects in context. Computer Vision – ECCV 2014, pp. 740–755 (Springer International Publishing, 2014). – reference: Mandal, S. & Uhlmann, V. SplineDist: automated cell segmentation with spline curves. In IEEE 18th International Symposium on Biomedical Imaging (ISBI) pp. 1082–1086 (IEEE, 2021). – reference: UpschulteEContour proposal networks for biomedical instance segmentationMed. Image Anal.20227710237110.1016/j.media.2022.10237135180674 – reference: HollandiRnucleAIzer: a parameter-free deep learning framework for nucleus segmentation using image style transferCell Syst.202010.545345810.1016/j.cels.2020.04.003 – reference: IsenseeFnnU-Net: a self-configuring method for deep learning-based biomedical image segmentationNat. Methods202118.220321110.1038/s41592-020-01008-z – reference: PangHA fully automatic segmentation pipeline of pulmonary lobes before and after lobectomy from computed tomography imagesComput. Biol. Med.202214710579210.1016/j.compbiomed.2022.10579235780601 – reference: He, K. et al. Deep residual learning for image recognition. 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Snippet	Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often...
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SubjectTerms	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
Title	Segmentation metric misinterpretations in bioimage analysis
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