Open AccessAutomated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography
Author(s) -
Shalev Ronny,
Nakamura Daisuke,
Nishino Setsu,
Rollins Andrew M.,
Bezerra Hiram G.,
Wilson David L.,
Ray Soumya
Publication year - 2017
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v38i1.2713
Subject(s) - optical coherence tomography , visualization , computer science , identification (biology) , vulnerable plaque , artificial intelligence , computer vision , coherence (philosophical gambling strategy) , process (computing) , key (lock) , radiology , medicine , pathology , quantum mechanics , biology , botany , physics , computer security , operating system
An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31 percent of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state‐of‐the‐art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is an offline approach where the images are first collected and then manually analyzed an image at a time to identify regions at risk of thrombosis. This process is extremely laborious, time consuming, and prone to human error. We are building a system that, when complete, will provide interactive three‐dimensional visualization of a blood vessel as an IVOCT is in progress. The visualization will highlight different plaque types and enable quick identification of regions at risk. In this article, we describe our approach, focusing on machine‐learning methods that are a key enabling technology. Our empirical results using real OCT data show that our approach can identify different plaque types efficiently with high accuracy across multiple patients.