Coronary artery disease (CAD) is the leading cause of death in the world. Most acute coronary events (e.g. heart attacks) are due to the rupture of atherosclerotic plaques inside the arteries, however, calcified lesion is the most widely treatable, typically, by stent implantation via percutaneous coronary intervention (PCI). Intravascular Optical Coherence Tomography (IVOCT) imaging has the resolution, contrast, and penetration depth to characterize coronary artery plaques. Conventional manual evaluation of IVOCT images, based on qualitative interpretation of image features, is tedious and time consuming. The aim of this PhD dissertation was to develop advanced algorithms to fully automate the task of plaque characterization, thereby significantly reduce image analysis time, enable intervention planning, and increase IVOCT data usability. We based our algorithms on machine learning combined with advanced image processing techniques.
We developed a processing pipeline on a 3D local region of support for estimation of optical properties of atherosclerotic plaques from coronary artery, IVOCT pullbacks. Performance was assessed in comparison with observer-defined standards using clinical pullback data. Values (calcium 3.58±1.74mm−¹, lipid 9.93±2.44mm−¹ and fibrous 1.96±1.11mm−¹) were consistent with previous measurements. We, then, created a method to automatically classify plaque tissues as fibrous, calcified, or lipid-rich. For this multi-class problem, we used one-versus-rest SVM classifiers for each of the three plaque types, rules to exclude many voxels called “other,” and both physics-inspired and local texture features to classify voxels. Experiments on the clinical training data yielded 5-fold, voxel-wise accuracy of 87.7±8.6%, 96.7±4.9% and 97.3±2.4% for calcified, lipid-rich and fibrotic tissues, respectively. Experiments on the independent validation data (ex-vivo image data accurately labeled using registered 3D microscopic cryo-imaging and was used as ground truth) yielded overall 87.1% accuracy indicating generalizability. This was followed by a development of a novel approach for real-time calcium segmentation. The trained algorithm was evaluated on the independent validation data. We achieved 5-fold cross validation calcium classification with F1 score of 93.7±2.7%, recall of ≥89%, precision of ≥97%, and running time of 2.6 seconds per frame suggesting possible on-line use.
We conclude with an application whose purpose is to be a complementary to the cardiologist in data analysis, off-line and on-line.
Keywords: Machine learning, support vector machine, texture classification, SVM, Image Processing, optical coherence tomography, plaque classification, calcium classification, intracoronary plaque, intravascular, OCT, CAD