Computer-aided detection of intracoronary stent in intravascular ultrasound sequences.

Francesco Ciompi,Simone Balocco,Josepa Mauri,Petia Radeva,Xavier Carrillo,Juan Rigla

doi:10.1118/1.4962927

Abstract

An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during percutaneous coronary intervention (PCI), in order to prevent acute vessel occlusion. The identification of struts location and the definition of the stent shape is relevant for PCI planning and for patient follow-up. The authors present a fully automatic framework for computer-aided detection (CAD) of intracoronary stents in intravascular ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape. The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classification. The output of the classification stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multicentric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bioabsorbable stents. The method was able to detect struts in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bioabsorbable stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts. The results are close to the interobserver variability and suggest that the system has the potential of being used as a method for aiding percutaneous interventions.

Highlights

An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during percutaneous coronary intervention (PCI) in order to scaffold the arterial wall after balloon angioplasty and to restore the blood flow to prevent acute vessel occlusion
The method presented by Gatta et al.20 is applied to the intravascular ultrasound (IVUS) pullback, which selects a sequence of gated frames G = { fg j} that are processed by the proposed computer-aided detection (CAD) system
In order to apply the trained method to IVUS sequences acquired at different frequencies and from different echograph producers, the semantic classifier and the strut kernel should be retrained. This may seem a limitation for the current approach, it is worth noting that a similar procedure was followed in the “Lumen + External Elastic Laminae Border Detection in IVUS” challenge,25 where we retrained our method developed with 40 MHz data using 20 MHz data from a different vendor, and we achieved the top performance among fully automatic methods for the detection of the media-adventitia at 20 MHz

Summary

Introduction

An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during percutaneous coronary intervention (PCI) in order to scaffold the arterial wall after balloon angioplasty and to restore the blood flow to prevent acute vessel occlusion. After x-ray guided stent placement, cases of underexpansion (stent is correctly apposed to the luminal wall but it is not completely expanded) or malapposition (the stent is not completely in contact with the luminal wall) may occur. The identification of struts location and the definition of the stent shape, compared with the luminal border and the vessel border, allow physicians to assess stent placement in the vessel and the need for a further balloon postdilatation. Intravascular ultrasound (IVUS), a catheter-based imaging technique that provides a sequence of tomographic images (pullback) of the internal vessel morphology [see Fig. 1(a)] represents a potential alternative. In an IVUS sequence the placement of a stent can be deduced by the position of its struts [see Fig. 1(b)].

Methods

Results

Conclusion