3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation
This work addresses the need for improved clinical evaluation of PCI stent deployment in coronary artery disease patients, though it appears incremental as it builds on existing segmentation techniques with a novel visualization approach.
The paper tackled the problem of assessing stent apposition in percutaneous coronary intervention (PCI) to prevent complications like in-stent restenosis, by proposing a 3D distance-color-coded assessment method using deep learning for segmentation in intravascular optical coherence tomography images, achieving over 95% segmentation precision.
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.