Segmentation of Anatomical Layers and Artifacts in Intravascular Polarization Sensitive Optical Coherence Tomography Using Attending Physician and Boundary Cardinality Losses

TL;DR

This paper introduces a CNN-based method with a novel loss function for segmenting anatomical layers and artifacts in intravascular PS-OCT images, improving accuracy over existing techniques.

Contribution

It presents a new multi-term loss function and a CNN model that enhances segmentation and artifact detection in intravascular PS-OCT images, advancing automated plaque analysis.

Findings

Outperforms state-of-the-art in layer classification

Successfully segments artifacts and thickened vessel regions

Provides publicly available source code and trained model

Abstract

Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses and provide critical vessel parameters to optimize percutaneous intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) simultaneously provides high-resolution cross-sectional images of vascular structures while also revealing preponderant tissue components such as collagen and smooth muscle and thereby enhances plaque characterization. Automated interpretation of these features promises to facilitate the objective clinical investigation of the natural history and significance of coronary atheromas. Here, we propose a convolutional neural network model, optimized using a new multi-term loss function, to classify the lumen, intima, and media layers in addition to the guidewire and plaque shadows. We demonstrate that our multi-class classification model outperforms state-of-the-art methods in detecting the coronary anatomical layers. Furthermore, the proposed model segments two classes of common imaging artifacts and detects the anatomical layers within the thickened vessel wall regions that were excluded from analysis by other studies. The source code and the trained model are publicly available at https://github.com/mhaft/OCTseg