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Evaluating Classifiers for Atherosclerotic Plaque Component Segmentation in MRI

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Medical Image Understanding and Analysis (MIUA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 723))

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Abstract

Segmentation of tissue components of atherosclerotic plaques in MRI is promising for improving future treatment strategies of cardiovascular diseases. Several methods have been proposed before with varying results. This study aimed to perform a structured comparison of various classifiers, training set sizes, and MR image sequences to determine the most promising strategy for methodology development. Five different classifiers (linear discriminant classifier (LDC), quadratic discriminant classifier (QDC), random forest (RF), and support vector classifiers with both a linear (SVM\(_{lin}\)) and radial basis function kernel (SVM\(_{rbf}\))) were evaluated. We used carotid MRI data from 124 symptomatic patients, scanned in 4 centres with 2 different MRI protocols (45 and 79 patients). Firstly, learning curves of accuracy as a function of increasing training data size showed stabilisation of performance after using \(\sim \)10–15 patients for training. Best results were found for LDC, QDC and RF. Intraplaque haemorrhage was most accurately classified in both protocols, and lowest accuracy was found for the lipid-rich necrotic core. Secondly, for LDC and RF it was shown that leaving out different MRI sequences usually negatively affects results for one or more classes. However, leaving out T2-weighted scans did not have a big impact. In conclusion, several classifiers obtain generally good results for classification of plaque components in MRI. Identification of intraplaque haemorrhage is the most promising, and lipid-rich necrotic core remains the most difficult.

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Acknowledgments

This research has been supported by an EPSRC Technology Strategy Board CR&D Grant (EP/L505304/1). The PARISK study was performed within the framework of the Center for Translational Molecular Medicine (www.ctmm.nl), project PARISK (Plaque At RISK; grant 01C-202) and supported by the Dutch Heart Foundation. This research was also partly funded by the Netherlands Organisation for Scientific Research (NWO).

The Division of Imaging Sciences also receives support from the Centre of Excellence in Medical Engineering (funded by the Welcome Trust and EPSRC; grant number WT 088641/Z/09/Z) and the Department of Health through the National Institute for Health Research (NIHR) Biomedical Research Centre award to Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London, and by the NIHR Healthcare Technology Co-operative for Cardiovascular Disease at Guys and St Thomas NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Authors and Affiliations

  1. Division of Imaging Sciences, Department of Biomedical Engineering, King’s College London, London, UK

    Arna van Engelen & Rene M. Botnar

  2. Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands

    Marleen de Bruijne & Wiro J. Niessen

  3. Philips Healthcare, Guildford, UK

    Torben Schneider

  4. Department of Radiology, Erasmus MC, Rotterdam, The Netherlands

    Anouk C. van Dijk

  5. Department of Radiology and Nuclear Medicine, CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands

    M. Eline Kooi

  6. Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands

    Jeroen Hendrikse

  7. Department of Radiology, Academic Medical Center Amsterdam, Amsterdam, The Netherlands

    Aart Nederveen

Authors
  1. Arna van Engelen
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Correspondence to Arna van Engelen .

Editor information

Editors and Affiliations

  1. Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom

    María Valdés Hernández

  2. University of León, León, Spain

    Víctor González-Castro

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van Engelen, A. et al. (2017). Evaluating Classifiers for Atherosclerotic Plaque Component Segmentation in MRI. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-60964-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60963-8

  • Online ISBN: 978-3-319-60964-5

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