Automatic stent reconstruction in optical coherence tomography based on a deep convolutional model.

Peng Wu,Jingfeng Bai,Wei Yang,Yingguang Li,Miao Chu,Wei Yu,Hélène Tauzin,William Wijns,Juan Luis Gutiérrez-Chico,Shengxian Tu,Nicolas Meneveau,Benoît Guillon

doi:10.1364/boe.390113

Abstract

Intravascular optical coherence tomography (IVOCT) can accurately assess stent apposition and expansion, thus enabling the optimisation of a stenting procedure to minimize the risk of device failure. This paper presents a deep convolutional based model for automatic detection and segmentation of stent struts. The input of pseudo-3D images aggregated the information from adjacent frames to refine the probability of strut detection. In addition, multi-scale shortcut connections were implemented to minimize the loss of spatial resolution and refine the segmentation of strut contours. After training, the model was independently tested in 21,363 cross-sectional images from 170 IVOCT image pullbacks. The proposed model obtained excellent segmentation (0.907 Dice and 0.838 Jaccard) and detection metrics (0.943 precision, 0.940 recall and 0.936 F1-score), significantly better than conventional features-based algorithms. This performance was robust and homogenous among IVOCT pullbacks with different sources of acquisition (clinical centres, imaging operators, type of stent, time of acquisition and challenging scenarios). In addition, excellent agreement between the model and a commercialized software was observed in the quantification of clinically relevant parameters. In conclusion, the deep-convolutional model can accurately detect stent struts in IVOCT images, thus enabling the fully-automatic quantification of stent parameters in an extremely short time. It might facilitate the application of quantitative IVOCT analysis in real-world clinical scenarios.

Highlights

Ischemic heart disease (IHD) is still today the first cause of mortality in the world, especially in developed countries [1,2,3]
The vast majority of cases are due to atherosclerosis, a complex systemic degenerative process resulting in cholesterol accumulation in the extra-cellular space of the arterial intima, with inflammation, foam-cells formation, and necrosis [4,5,6,7]
Malapposition is associated with delayed neointimalisation [10,12,13], which is one of the pathological substrates for stent thrombosis [14], whilst underexpansion is associated with both restenosis [15,16,17,18,19] and stent thrombosis [20]

Summary

Introduction

Ischemic heart disease (IHD) is still today the first cause of mortality in the world, especially in developed countries [1,2,3]. The vast majority of cases are due to atherosclerosis, a complex systemic degenerative process resulting in cholesterol accumulation in the extra-cellular space of the arterial intima, with inflammation, foam-cells formation, and necrosis [4,5,6,7]. Percutaneous coronary intervention (PCI) with implantation of stents has become the treatment of choice for most cases of IHD in any clinical presentation [9]. The stent itself constitutes an insult for the vascular tissue, eliciting a healing reaction that might result in stent failure. With excessive neointimal proliferation is the most common substrate for stent restenosis, whilst an insufficient neointimalisation and reendothelialisation may trigger stent thrombosis [10]. Malapposition is associated with delayed neointimalisation [10,12,13], which is one of the pathological substrates for stent thrombosis [14], whilst underexpansion is associated with both restenosis [15,16,17,18,19] and stent thrombosis [20]

Methods

Results

Discussion

Conclusion