Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks

TL;DR

This paper develops a deep learning-based system for automatic detection and differentiation of plaques in intravascular OCT images, achieving high accuracy and demonstrating feasibility for clinical decision support.

Contribution

It introduces a new dataset with expert labels and explores advanced deep learning models, transfer learning, and multi-representation fusion for plaque detection and differentiation.

Findings

Achieved 91.7% accuracy in plaque detection

Demonstrated effective use of multi-representation fusion

Showed deep learning's potential for clinical decision support

Abstract

Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient which requires automatic plaque detection for fast and accurate decision support. So far, automatic approaches rely on classic machine learning methods and deep learning solutions have rarely been studied. Given the success of deep learning methods with other imaging modalities, a thorough understanding of deep learning-based plaque detection for future clinical decision support systems is required. We address this issue with a new dataset consisting of in-vivo patient images labeled by three trained experts. Using this dataset, we employ state-of-the-art deep learning models that directly learn plaque classification from the images. For improved performance, we study different transfer learning approaches. Furthermore, we investigate the use of cartesian and polar image representations and employ data augmentation techniques tailored to each representation. We fuse both representations in a multi-path architecture for more effective feature exploitation. Last, we address the challenge of plaque differentiation in addition to detection. Overall, we find that our combined model performs best with an accuracy of 91.7%, a sensitivity of 90.9% and a specificity of 92.4%. Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasible.