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

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Title: Automatic quality control of cardiac MRI segmentation in large-scale population imaging
Authors: Robinson, R
Valindria, V
Bai, W
Suzuki, H
Matthews, P
Page, C
Rueckert, D
Glocker, B
Item Type: Conference Paper
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.
Issue Date: 4-Sep-2017
Date of Acceptance: 16-May-2017
URI: http://hdl.handle.net/10044/1/49164
DOI: 10.1007/978-3-319-66182-7_82
ISSN: 0302-9743
Publisher: Springer
Start Page: 720
End Page: 727
Journal / Book Title: Lecture Notes in Computer Science
Volume: 10433
Copyright Statement: © 2017 Springer International Publishing AG 2017. The final authenticated version is available online at https://doi.org/10.1007/978-3-319-66182-7_82
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
GlaxoSmithKline
Engineering & Physical Science Research Council (EPSRC)
Biogen Idec Ltd
UK DRI Ltd
Medical Research Council (MRC)
Medical Research Council (MRC)
UK DRI Ltd
Funder's Grant Number: EP/P001009/1
EP/N014529/1
PO 11024
4050641385
MR/M024903/1
4050641385
N/A
Conference Name: Medical Image Computing and Computer Assisted Intervention - MICCAI 2017
Keywords: Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2017-09-11
Finish Date: 2017-09-13
Conference Place: Quebec, Canada
Online Publication Date: 2017-09-04
Appears in Collections:Computing
Department of Brain Sciences
Faculty of Engineering