Automatic quality control of cardiac MRI segmentation in large-scale population imaging

Robert Robinson; Vanya V. Valindria; Wenjia Bai; Hideaki Suzuki; Paul M. Matthews; Chris Page; Daniel Rueckert; Ben Glocker

doi:10.1007/978-3-319-66182-7_82

Automatic quality control of cardiac MRI segmentation in large-scale population imaging

Robert Robinson, Vanya V. Valindria, Wenjia Bai, Hideaki Suzuki, Paul M. Matthews, Chris Page, Daniel Rueckert, Ben Glocker

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

23 Zitate (Scopus)

Abstract

The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study.

Originalsprache	Englisch
Titel	Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
Redakteure/-innen	Maxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein
Herausgeber (Verlag)	Springer Verlag
Seiten	720-727
Seitenumfang	8
ISBN (Print)	9783319661810
DOIs	https://doi.org/10.1007/978-3-319-66182-7_82
Publikationsstatus	Veröffentlicht - 2017
Extern publiziert	Ja
Veranstaltung	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Kanada Dauer: 11 Sept. 2017 → 13 Sept. 2017

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	10433 LNCS
ISSN (Print)	0302-9743
ISSN (elektronisch)	1611-3349

Konferenz

Konferenz	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Land/Gebiet	Kanada
Ort	Quebec City
Zeitraum	11/09/17 → 13/09/17

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10.1007/978-3-319-66182-7_82

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Robinson, R., Valindria, V. V., Bai, W., Suzuki, H., Matthews, P. M., Page, C., Rueckert, D., & Glocker, B. (2017). Automatic quality control of cardiac MRI segmentation in large-scale population imaging. in M. Descoteaux, S. Duchesne, A. Franz, P. Jannin, D. L. Collins, & L. Maier-Hein (Hrsg.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (S. 720-727). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10433 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66182-7_82

Robinson, Robert ; Valindria, Vanya V. ; Bai, Wenjia et al. / Automatic quality control of cardiac MRI segmentation in large-scale population imaging. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Hrsg. / Maxime Descoteaux ; Simon Duchesne ; Alfred Franz ; Pierre Jannin ; D. Louis Collins ; Lena Maier-Hein. Springer Verlag, 2017. S. 720-727 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Automatic quality control of cardiac MRI segmentation in large-scale population imaging",

abstract = "The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study.",

author = "Robert Robinson and Valindria, {Vanya V.} and Wenjia Bai and Hideaki Suzuki and Matthews, {Paul M.} and Chris Page and Daniel Rueckert and Ben Glocker",

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doi = "10.1007/978-3-319-66182-7_82",

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Robinson, R, Valindria, VV, Bai, W, Suzuki, H, Matthews, PM, Page, C, Rueckert, D & Glocker, B 2017, Automatic quality control of cardiac MRI segmentation in large-scale population imaging. in M Descoteaux, S Duchesne, A Franz, P Jannin, DL Collins & L Maier-Hein (Hrsg.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 10433 LNCS, Springer Verlag, S. 720-727, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Kanada, 11/09/17. https://doi.org/10.1007/978-3-319-66182-7_82

Automatic quality control of cardiac MRI segmentation in large-scale population imaging. / Robinson, Robert; Valindria, Vanya V.; Bai, Wenjia et al.
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Hrsg. / Maxime Descoteaux; Simon Duchesne; Alfred Franz; Pierre Jannin; D. Louis Collins; Lena Maier-Hein. Springer Verlag, 2017. S. 720-727 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10433 LNCS).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Automatic quality control of cardiac MRI segmentation in large-scale population imaging

AU - Robinson, Robert

AU - Valindria, Vanya V.

AU - Bai, Wenjia

AU - Suzuki, Hideaki

AU - Matthews, Paul M.

AU - Page, Chris

AU - Rueckert, Daniel

AU - Glocker, Ben

PY - 2017

Y1 - 2017

N2 - The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study.

AB - The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study.

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AN - SCOPUS:85029358993

SN - 9783319661810

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 720

EP - 727

BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings

A2 - Descoteaux, Maxime

A2 - Duchesne, Simon

A2 - Franz, Alfred

A2 - Jannin, Pierre

A2 - Collins, D. Louis

A2 - Maier-Hein, Lena

PB - Springer Verlag

T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017

Y2 - 11 September 2017 through 13 September 2017

ER -

Robinson R, Valindria VV, Bai W, Suzuki H, Matthews PM, Page C et al. Automatic quality control of cardiac MRI segmentation in large-scale population imaging. in Descoteaux M, Duchesne S, Franz A, Jannin P, Collins DL, Maier-Hein L, Hrsg., Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Springer Verlag. 2017. S. 720-727. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66182-7_82

Automatic quality control of cardiac MRI segmentation in large-scale population imaging

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