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2017 | Supplement | Buchkapitel

Automatic Quality Control of Cardiac MRI Segmentation in Large-Scale Population Imaging

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

Erschienen in: Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

Verlag: Springer International Publishing

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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.

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Fußnoten
1
UK Biobank Resource under Application Number 12579.
 
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Metadaten
Titel
Automatic Quality Control of Cardiac MRI Segmentation in Large-Scale Population Imaging
verfasst von
Robert Robinson
Vanya V. Valindria
Wenjia Bai
Hideaki Suzuki
Paul M. Matthews
Chris Page
Daniel Rueckert
Ben Glocker
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-66182-7_82