Work

Automatic classification of atherosclerotic plaques imaged with intravascular OCT

Year: 2016

Type: article

Abstract: Intravascular optical coherence tomography (IV-OCT) allows evaluation of atherosclerotic plaques; however, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present a novel computational method for automated IV-OCT plaque characterization. This method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The performance of our proposed method was evaluated with IV-OCT scans of cadaveric human coronary arteries and corresponding tissue histopathology. Our results suggest that this methodology allows automated identification of fibrotic and lipid-containing plaques. Moreover, this novel computational method has the potential to enable high throughput atherosclerotic plaque characterization.

Source: Biomedical Optics Express

DOI: 10.1364/boe.7.004069

Authors: Jose J. Rico-Jimenez, Daniel U. Campos-Delgado, Martin Villiger, Kenichiro Otsuka, Brett E. Bouma, Javier A. Jo

Institutions: Texas A&M University, Autonomous University of San Luis Potosí, Harvard University, Massachusetts General Hospital

Language: English

Topic: Coronary Interventions and Diagnostics

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,913