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Athanasiou LS, Rigas G, Sakellarios A, et al. Error propagation in the characterization of atheromatic plaque types based on imaging. Comput Methods Programs Biomed. 2015;121(3):161-74doi: 10.1016/j.cmpb.2015.06.002.
Athanasiou, L. S., Rigas, G., Sakellarios, A., Bourantas, C. V., Stefanou, K., Fotiou, E., Exarchos, T. P., Siogkas, P., Naka, K. K., Parodi, O., Vozzi, F., Teng, Z., Young, V. E., Gillard, J. H., Prati, F., Michalis, L. K., & Fotiadis, D. I. (2015). Error propagation in the characterization of atheromatic plaque types based on imaging. Computer methods and programs in biomedicine, 121(3), 161-74. https://doi.org/10.1016/j.cmpb.2015.06.002
Athanasiou, Lambros S, et al. "Error propagation in the characterization of atheromatic plaque types based on imaging." Computer methods and programs in biomedicine vol. 121,3 (2015): 161-74. doi: https://doi.org/10.1016/j.cmpb.2015.06.002
Athanasiou LS, Rigas G, Sakellarios A, Bourantas CV, Stefanou K, Fotiou E, Exarchos TP, Siogkas P, Naka KK, Parodi O, Vozzi F, Teng Z, Young VE, Gillard JH, Prati F, Michalis LK, Fotiadis DI. Error propagation in the characterization of atheromatic plaque types based on imaging. Comput Methods Programs Biomed. 2015 Oct;121(3):161-74. doi: 10.1016/j.cmpb.2015.06.002. Epub 2015 Jul 02. PMID: 26165637.
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Comput Methods Programs Biomed. 2015 Oct;121(3):161-74. doi: 10.1016/j.cmpb.2015.06.002. Epub 2015 Jul 02.

Error propagation in the characterization of atheromatic plaque types based on imaging.

Computer methods and programs in biomedicine

Lambros S Athanasiou, George Rigas, Antonis Sakellarios, Christos V Bourantas, Kostas Stefanou, Evangelos Fotiou, Themis P Exarchos, Panagiotis Siogkas, Katerina K Naka, Oberdan Parodi, Federico Vozzi, Zhongzhao Teng, Victoria E L Young, Jonathan H Gillard, Francesco Prati, Lampros K Michalis, Dimitrios I Fotiadis

Affiliations

  1. Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece.
  2. ThoraxCenter, Erasmus Medical Center, 's-Gravendijkwal 230, 3015 CE Rotterdam, The Netherlands.
  3. Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; FORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, GR 45110 Ioannina, Greece.
  4. Michaelidion Cardiac Center, Department of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece.
  5. Institute of Clinical Physiology, National Research Council, Pisa 56124, Italy.
  6. University Department of Radiology, University of Cambridge, Cambridge CB20QQ, UK.
  7. Interventional Cardiology, San Giovanni Hospital, Via dell' Amba Aradam, 8, Rome 00184, Italy.
  8. Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; FORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, GR 45110 Ioannina, Greece. Electronic address: [email protected].

PMID: 26165637 DOI: 10.1016/j.cmpb.2015.06.002

Abstract

Imaging systems transmit and acquire signals and are subject to errors including: error sources, signal variations or possible calibration errors. These errors are included in all imaging systems for atherosclerosis and are propagated to methodologies implemented for the segmentation and characterization of atherosclerotic plaque. In this paper, we present a study for the propagation of imaging errors and image segmentation errors in plaque characterization methods applied to 2D vascular images. More specifically, the maximum error that can be propagated to the plaque characterization results is estimated, assuming worst-case scenarios. The proposed error propagation methodology is validated using methods applied to real datasets, obtained from intravascular imaging (IVUS) and optical coherence tomography (OCT) for coronary arteries, and magnetic resonance imaging (MRI) for carotid arteries. The plaque characterization methods have recently been presented in the literature and are able to detect the vessel borders, and characterize the atherosclerotic plaque types. Although, these methods have been extensively validated using as gold standard expert annotations, by applying the proposed error propagation methodology a more realistic validation is performed taking into account the effect of the border detection algorithms error and the image formation error into the final results. The Pearson's coefficient of the detected plaques has changed significantly when the method was applied to IVUS and OCT, while there was not any variation when the method was applied to MRI data.

Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Keywords: Atherosclerotic plaque; Error propagation; Image formation; Intravascular imaging; Magnetic resonance imaging; Optical coherence tomography

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