Automated quality control in image segmentation: Application to the UK Biobank cardiovascular magnetic resonance imaging study

Robert Robinson, Vanya V. Valindria, Wenjia Bai, Ozan Oktay, Bernhard Kainz, Hideaki Suzuki, Mihir M. Sanghvi, Nay Aung, José Miguel Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron M. Lee, Valentina Carapella, Young Jin Kim, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Chris Page, Paul M. MatthewsDaniel Rueckert, Ben Glocker

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22 Citations (Scopus)

Abstract

Background: 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 automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. Methods: 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 segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

Original languageEnglish
Article number18
JournalJournal of Cardiovascular Magnetic Resonance
Volume21
Issue number1
DOIs
Publication statusPublished - 2019 Mar 14

Bibliographical note

Funding Information:
Steffen E. Petersen provides consultancy to Circle Cardiovascular Imaging Inc. (Calgary, Alberta, Canada). Ben Glocker receives research funding from HeartFlow Inc. (Redwood City, CA, USA).

Funding Information:
RR is funded by both the King’s College London & Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging (EP/L015226/1) and GlaxoSmithKline; VV by Indonesia Endowment for Education (LPDP) Indonesian Presidential PhD Scholarship and HS by Research Fellowship from Uehara Memorial Foundation. This work was also supported by the following institutions: KF is supported by The Medical College of Saint Bartholomew’s Hospital Trust, an independent registered charity that promotes and advances medical and dental education and research at Barts and The London School of Medicine and Dentistry. AL and SEP acknowledge support from the NIHR Barts Biomedical Research Centre and from the “SmartHeart” EPSRC program grant (EP/P001009/ 1). SN and SKP are supported by the Oxford NIHR Biomedical Research Centre and the Oxford British Heart Foundation Centre of Research Excellence. This project was enabled through access to the MRC eMedLab Medical Bioinformatics infrastructure, supported by the Medical Research Council (grant number MR/L016311/1). NA is supported by a Wellcome Trust Research Training Fellowship (203553/Z/Z). The authors SEP, SN and SKP acknowledge the British Heart Foundation (BHF) for funding the manual analysis to create a cardiovascular magnetic resonance imaging reference standard for the UKBB imaging resource in 5000 CMR scans (PG/14/89/31194). PMM gratefully acknowledges support from the Edmond J. Safra Foundation and Lily Safra, the Imperial College Healthcare Trust Biomedical Research Centre, the EPSRC Centre for Mathematics in Precision Healthcare, the UK Dementia Research Institute and the MRC. BG received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 757173, project MIRA, ERC-2017-STG).

Publisher Copyright:
© 2019 The Author(s).

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

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