3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation

TL;DR

This paper introduces a deep-learning-based 3D assessment method for PCI stent apposition that accurately segments vessels and stents in IV-OCT images, providing a visual 3D color-coded map to improve clinical evaluation.

Contribution

It presents a novel 3D distance-color-coded assessment technique using deep learning for precise segmentation and visualization of stent apposition in IV-OCT images.

Findings

Achieved over 95% segmentation precision.

Enhanced visualization of stent-lumen distances.

Supports personalized PCI treatment planning.

Abstract

Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent apposition holds pivotal importance in averting and identifying PCI complications that lead to in-stent restenosis. Here we proposed a novel three-dimensional (3D) distance-color-coded assessment (DccA)for PCI stent apposition via deep-learning-based 3D multi-object segmentation in intravascular optical coherence tomography (IV-OCT). Our proposed 3D DccA accurately segments 3D vessel lumens and stents in IV-OCT images, using a spatial matching network and dual-layer training with style transfer. It quantifies and maps stent-lumen distances into a 3D color space, facilitating 3D visual assessment of PCI stent apposition. Achieving over 95% segmentation precision, our proposed DccA enhances clinical evaluation of PCI stent deployment and supports personalized treatment planning.