Automatic characterization of plaques and tissue in IVOCT images using a multi-step convolutional neural network framework

Abstract

Intravascular optical coherence tomography (IVOCT) is a light-based imaging modality of great interest because it can contribute in diagnosing and preventing atherosclerosis due to its ability to provide in vivo insight of coronary arteries’ morphology. The substantial number of slices which are obtained per artery, makes it laborious for medical experts to classify image regions of interest. We propose a framework based on Convolutional Neural Networks (CNN) for classification of regions of intravascular OCT images into 4 categories: fibrous tissue, mixed plaque, lipid plaque and calcified plaque. The framework consists of 2 main parts. In the first part, square patches (8 × 8 pixels) of OCT images are classified as fibrous tissue or plaque using a CNN which was designed for texture classification. In the second part, larger regions consisting of adjacent patches which are classified as plaque in the first part, are classified in 3 categories: lipid, calcium, mixed. Region classification is implemented by an AlexNet version re-trained on images artificially constructed to depict only the core of the plaque region which is considered as its blueprint. Various simple steps like thresholding and morphological operations are used through the framework, mainly to exclude background from analysis and to merge patches into regions. The first results are promising since the classification accuracy of the two networks is high (95% and 89% respectively).