Error propagation in the characterization of atheromatic plaque types based on imaging
Computer Methods and Programs in Biomedicine , Volume 121 - Issue 3 p. 161- 174
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.
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|Computer Methods and Programs in Biomedicine|
|Organisation||Erasmus MC: University Medical Center Rotterdam|
Athanasiou, L.S, Rigas, G, Sakellarios, A.I, Bourantas, C.V, Stefanou, K, Fotiou, E, … Fotiadis, D.I. (2015). Error propagation in the characterization of atheromatic plaque types based on imaging. Computer Methods and Programs in Biomedicine, 121(3), 161–174. doi:10.1016/j.cmpb.2015.06.002