Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results

Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibrati...

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Veröffentlicht in:	Computers in biology and medicine Jg. 197; H. Pt B; S. 111024
Hauptverfasser:	Riera-Marín, Meritxell, O.K., Sikha, Rodríguez-Comas, Júlia, May, Matthias Stefan, Pan, Zhaohong, Zhou, Xiang, Liang, Xiaokun, Erick, Franciskus Xaverius, Prenner, Andrea, Hémon, Cédric, Boussot, Valentin, Dillenseger, Jean-Louis, Nunes, Jean-Claude, Qayyum, Abdul, Mazher, Moona, Niederer, Steven A., Kushibar, Kaisar, Martín-Isla, Carlos, Radeva, Petia, Lekadir, Karim, Barfoot, Theodore, Garcia Peraza Herrera, Luis C., Glocker, Ben, Vercauteren, Tom, Gago, Lucas, Englemann, Justin, Kleiss, Joy-Marie, Aubanell, Anton, Antolin, Andreu, García-López, Javier, González Ballester, Miguel A., Galdrán, Adrián
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.10.2025 Elsevier
Schlagworte:	Abdominal CT Artificial Intelligence Bioengineering Calibration Computer Science Deep Learning Engineering Sciences Humans Image Processing, Computer-Assisted - methods Imaging Internal Medicine Life Sciences Medical Imaging Multi-class image segmentation Multiple expert annotations Other Signal and Image processing Uncertainty Multiple expert annotations Uncertainty Multi-class image segmentation Calibration Abdominal CT abdominal CT
ISSN:	0010-4825, 1879-0534, 1879-0534
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Abstract	Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS), which highlights the critical role of multiple annotators in establishing a more comprehensive ground truth, emphasizing that segmentation is inherently subjective and that leveraging inter-annotator variability is essential for robust model evaluation. Seven teams participated in the challenge, submitting a variety of DL models evaluated using metrics such as Dice Similarity Coefficient (DSC), Expected Calibration Error (ECE), and Continuous Ranked Probability Score (CRPS). By incorporating consensus and dissensus ground truth, we assess how DL models handle uncertainty and whether their confidence estimates align with true segmentation performance. Our findings reinforce the importance of well-calibrated models, as better calibration is strongly correlated with the quality of the results. Furthermore, we demonstrate that segmentation models trained on diverse datasets and enriched with pre-trained knowledge exhibit greater robustness, particularly in cases deviating from standard anatomical structures. Notably, the best-performing models achieved high DSC and well-calibrated uncertainty estimates. This work underscores the need for multi-annotator ground truth, thorough calibration assessments, and uncertainty-aware evaluations to develop trustworthy and clinically reliable DL-based medical image segmentation models. •The CURVAS challenge assessed DL segmentation under annotation uncertainty.•Multi-rater CT annotations evaluated consensus and disagreement in three organs.•Metrics account for expert variability, enabling more reliable model assessment.•Calibration-aware models showed strong links between confidence and accuracy.•Volume estimation and robustness to ambiguity support clinical applicability.
AbstractList	Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS), which highlights the critical role of multiple annotators in establishing a more comprehensive ground truth, emphasizing that segmentation is inherently subjective and that leveraging inter-annotator variability is essential for robust model evaluation. Seven teams participated in the challenge, submitting a variety of DL models evaluated using metrics such as Dice Similarity Coefficient (DSC), Expected Calibration Error (ECE), and Continuous Ranked Probability Score (CRPS). By incorporating consensus and dissensus ground truth, we assess how DL models handle uncertainty and whether their confidence estimates align with true segmentation performance. Our findings reinforce the importance of well-calibrated models, as better calibration is strongly correlated with the quality of the results. Furthermore, we demonstrate that segmentation models trained on diverse datasets and enriched with pre-trained knowledge exhibit greater robustness, particularly in cases deviating from standard anatomical structures. Notably, the best-performing models achieved high DSC and well-calibrated uncertainty estimates. This work underscores the need for multi-annotator ground truth, thorough calibration assessments, and uncertainty-aware evaluations to develop trustworthy and clinically reliable DL-based medical image segmentation models. Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS), which highlights the critical role of multiple annotators in establishing a more comprehensive ground truth, emphasizing that segmentation is inherently subjective and that leveraging inter-annotator variability is essential for robust model evaluation. Seven teams participated in the challenge, submitting a variety of DL models evaluated using metrics such as Dice Similarity Coefficient (DSC), Expected Calibration Error (ECE), and Continuous Ranked Probability Score (CRPS). By incorporating consensus and dissensus ground truth, we assess how DL models handle uncertainty and whether their confidence estimates align with true segmentation performance. Our findings reinforce the importance of well-calibrated models, as better calibration is strongly correlated with the quality of the results. Furthermore, we demonstrate that segmentation models trained on diverse datasets and enriched with pre-trained knowledge exhibit greater robustness, particularly in cases deviating from standard anatomical structures. Notably, the best-performing models achieved high DSC and well-calibrated uncertainty estimates. This work underscores the need for multi-annotator ground truth, thorough calibration assessments, and uncertainty-aware evaluations to develop trustworthy and clinically reliable DL-based medical image segmentation models.Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS), which highlights the critical role of multiple annotators in establishing a more comprehensive ground truth, emphasizing that segmentation is inherently subjective and that leveraging inter-annotator variability is essential for robust model evaluation. Seven teams participated in the challenge, submitting a variety of DL models evaluated using metrics such as Dice Similarity Coefficient (DSC), Expected Calibration Error (ECE), and Continuous Ranked Probability Score (CRPS). By incorporating consensus and dissensus ground truth, we assess how DL models handle uncertainty and whether their confidence estimates align with true segmentation performance. Our findings reinforce the importance of well-calibrated models, as better calibration is strongly correlated with the quality of the results. Furthermore, we demonstrate that segmentation models trained on diverse datasets and enriched with pre-trained knowledge exhibit greater robustness, particularly in cases deviating from standard anatomical structures. Notably, the best-performing models achieved high DSC and well-calibrated uncertainty estimates. This work underscores the need for multi-annotator ground truth, thorough calibration assessments, and uncertainty-aware evaluations to develop trustworthy and clinically reliable DL-based medical image segmentation models. Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS), which highlights the critical role of multiple annotators in establishing a more comprehensive ground truth, emphasizing that segmentation is inherently subjective and that leveraging inter-annotator variability is essential for robust model evaluation. Seven teams participated in the challenge, submitting a variety of DL models evaluated using metrics such as Dice Similarity Coefficient (DSC), Expected Calibration Error (ECE), and Continuous Ranked Probability Score (CRPS). By incorporating consensus and dissensus ground truth, we assess how DL models handle uncertainty and whether their confidence estimates align with true segmentation performance. Our findings reinforce the importance of well-calibrated models, as better calibration is strongly correlated with the quality of the results. Furthermore, we demonstrate that segmentation models trained on diverse datasets and enriched with pre-trained knowledge exhibit greater robustness, particularly in cases deviating from standard anatomical structures. Notably, the best-performing models achieved high DSC and well-calibrated uncertainty estimates. This work underscores the need for multi-annotator ground truth, thorough calibration assessments, and uncertainty-aware evaluations to develop trustworthy and clinically reliable DL-based medical image segmentation models. •The CURVAS challenge assessed DL segmentation under annotation uncertainty.•Multi-rater CT annotations evaluated consensus and disagreement in three organs.•Metrics account for expert variability, enabling more reliable model assessment.•Calibration-aware models showed strong links between confidence and accuracy.•Volume estimation and robustness to ambiguity support clinical applicability. AbstractDeep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS), which highlights the critical role of multiple annotators in establishing a more comprehensive ground truth, emphasizing that segmentation is inherently subjective and that leveraging inter-annotator variability is essential for robust model evaluation. Seven teams participated in the challenge, submitting a variety of DL models evaluated using metrics such as Dice Similarity Coefficient (DSC), Expected Calibration Error (ECE), and Continuous Ranked Probability Score (CRPS). By incorporating consensus and dissensus ground truth, we assess how DL models handle uncertainty and whether their confidence estimates align with true segmentation performance. Our findings reinforce the importance of well-calibrated models, as better calibration is strongly correlated with the quality of the results. Furthermore, we demonstrate that segmentation models trained on diverse datasets and enriched with pre-trained knowledge exhibit greater robustness, particularly in cases deviating from standard anatomical structures. Notably, the best-performing models achieved high DSC and well-calibrated uncertainty estimates. This work underscores the need for multi-annotator ground truth, thorough calibration assessments, and uncertainty-aware evaluations to develop trustworthy and clinically reliable DL-based medical image segmentation models.
ArticleNumber	111024
Author	O.K., Sikha Riera-Marín, Meritxell Dillenseger, Jean-Louis Vercauteren, Tom Boussot, Valentin Kleiss, Joy-Marie Antolin, Andreu García-López, Javier Zhou, Xiang Barfoot, Theodore Gago, Lucas Mazher, Moona Liang, Xiaokun May, Matthias Stefan Pan, Zhaohong Hémon, Cédric González Ballester, Miguel A. Qayyum, Abdul Nunes, Jean-Claude Lekadir, Karim Englemann, Justin Martín-Isla, Carlos Niederer, Steven A. Rodríguez-Comas, Júlia Erick, Franciskus Xaverius Prenner, Andrea Radeva, Petia Aubanell, Anton Garcia Peraza Herrera, Luis C. Kushibar, Kaisar Glocker, Ben Galdrán, Adrián
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Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China – sequence: 6 givenname: Xiang orcidid: 0009-0009-2843-4605 surname: Zhou fullname: Zhou, Xiang organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China – sequence: 7 givenname: Xiaokun orcidid: 0000-0002-1207-5726 surname: Liang fullname: Liang, Xiaokun organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China – sequence: 8 givenname: Franciskus Xaverius orcidid: 0000-0001-6004-7896 surname: Erick fullname: Erick, Franciskus Xaverius organization: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany – sequence: 9 givenname: Andrea orcidid: 0009-0004-8485-7702 surname: Prenner fullname: Prenner, Andrea organization: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany – sequence: 10 givenname: Cédric orcidid: 0009-0003-6669-5108 surname: Hémon fullname: Hémon, Cédric organization: Université de Rennes 1, CLCC Eugène Marquis, and INSERM UMR 1099 LTSI, Rennes, France – sequence: 11 givenname: Valentin orcidid: 0009-0003-2465-5458 surname: Boussot fullname: Boussot, Valentin organization: Université de Rennes 1, CLCC Eugène Marquis, and INSERM UMR 1099 LTSI, Rennes, France – sequence: 12 givenname: Jean-Louis orcidid: 0000-0001-8840-3944 surname: Dillenseger fullname: Dillenseger, Jean-Louis organization: Université de Rennes 1, CLCC Eugène Marquis, and INSERM UMR 1099 LTSI, Rennes, France – sequence: 13 givenname: Jean-Claude orcidid: 0000-0001-6560-1518 surname: Nunes fullname: Nunes, Jean-Claude organization: Université de Rennes 1, CLCC Eugène Marquis, and INSERM UMR 1099 LTSI, Rennes, France – sequence: 14 givenname: Abdul orcidid: 0000-0003-3102-1595 surname: Qayyum fullname: Qayyum, Abdul organization: National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom – sequence: 15 givenname: Moona orcidid: 0000-0003-4444-5776 surname: Mazher fullname: Mazher, Moona organization: Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom – sequence: 16 givenname: Steven A. orcidid: 0000-0002-4612-6982 surname: Niederer fullname: Niederer, Steven A. organization: Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom – sequence: 17 givenname: Kaisar orcidid: 0000-0001-7507-5208 surname: Kushibar fullname: Kushibar, Kaisar organization: Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain – sequence: 18 givenname: Carlos orcidid: 0000-0003-4270-4009 surname: Martín-Isla fullname: Martín-Isla, Carlos organization: Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain – sequence: 19 givenname: 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Snippet	Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models... AbstractDeep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these...
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SubjectTerms	Abdominal CT Artificial Intelligence Bioengineering Calibration Computer Science Deep Learning Engineering Sciences Humans Image Processing, Computer-Assisted - methods Imaging Internal Medicine Life Sciences Medical Imaging Multi-class image segmentation Multiple expert annotations Other Signal and Image processing Uncertainty
Title	Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
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