Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound
Sheet D, Karamalis A, Eslami A, Noel P, Chatterjee J, Ray AK, Laine AF, Carlier SG, Navab N, Katouzian A (2014)
Publication Type: Journal article
Publication year: 2014
Journal
Book Volume: 18
Pages Range: 103-117
Journal Issue: 1
DOI: 10.1016/j.media.2013.10.002
Abstract
Intravascular Ultrasound (IVUS) is a predominant imaging modality in interventional cardiology. It provides real-time cross-sectional images of arteries and assists clinicians to infer about atherosclerotic plaques composition. These plaques are heterogeneous in nature and constitute fibrous tissue, lipid deposits and calcifications. Each of these tissues backscatter ultrasonic pulses and are associated with a characteristic intensity in B-mode IVUS image. However, clinicians are challenged when colocated heterogeneous tissue backscatter mixed signals appearing as non-unique intensity patterns in B-mode IVUS image. Tissue characterization algorithms have been developed to assist clinicians to identify such heterogeneous tissues and assess plaque vulnerability. In this paper, we propose a novel technique coined as Stochastic Driven Histology (SDH) that is able to provide information about co-located heterogeneous tissues. It employs learning of tissue specific ultrasonic backscattering statistical physics and signal confidence primal from labeled data for predicting heterogeneous tissue composition in plaques. We employ a random forest for the purpose of learning such a primal using sparsely labeled and noisy samples. In clinical deployment, the posterior prediction of different lesions constituting the plaque is estimated. Folded cross-validation experiments have been performed with 53 plaques indicating high concurrence with traditional tissue histology. On the wider horizon, this framework enables learning of tissue-energy interaction statistical physics and can be leveraged for promising clinical applications requiring tissue characterization beyond the application demonstrated in this paper. © 2013 Elsevier B.V.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Indian Institute of Technology Bombay
India (IN)
Columbia University
United States (USA) (US)
Germany (DE)
Indian Institute of Technology Bombay
India (IN)
Columbia University
United States (USA) (US)
How to cite
APA:
Sheet, D., Karamalis, A., Eslami, A., Noel, P., Chatterjee, J., Ray, A.K.,... Katouzian, A. (2014). Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound. Medical Image Analysis, 18(1), 103-117. https://doi.org/10.1016/j.media.2013.10.002
MLA:
Sheet, Debdoot, et al. "Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound." Medical Image Analysis 18.1 (2014): 103-117.
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