Uncovering unstable plaques: deep learning segmentation in optical coherence tomography

Abstract

One of the primary objectives in modern cardiology is to analyze the risk of acute coronary syndrome (ACS) in patients with ischemic heart disease to develop preventive measures and determine the optimal treatment strategy. This study aims to develop an automated approach for the timely detection of significant, rupture-prone coronary lesions (unstable plaques) to prevent ACS. We collected optical coherence tomography (OCT) volumes from 34 patients, with each OCT volume representing an RGB video of 704×704 pixels per frame, acquired over a certain depth. After filtering and manual annotation, 11,771 images were obtained to identify four types of objects: Lumen, Fibrous cap, Lipid core, and Vasa vasorum. To segment and quantitatively assess these features, we configured and evaluated the performance of nine deep learning models (U-Net, LinkNet, FPN, PSPNet, DeepLabV3, PAN, MA-Net, U-Net++, DeepLabV3++). The study presents two approaches for training the aforementioned models: 1) detecting all analyzed objects and 2) applying a cascade of neural network models to separately detect subsets of objects. The results demonstrate the superiority of the cascade approach for analyzing OCT images. The combined use of PAN and MA-Net models achieved the highest average Dice similarity coefficient (DSC) of 0.721.