Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images
Introduction
Intravascular ultrasound (IVUS) is the preferred modality for assessing lumen and vessel wall dimensions, quantifying plaque burden and guiding revascularisation in complex lesions and high-risk patients. [1] Cumulative data have underscored its value in optimising procedural results and improving outcomes in patients undergoing percutaneous coronary intervention (PCI). [[2], [3], [4]] Its broader use, however, has been limited by the time and cost needed for acquiring and analysing IVUS imaging. Whilst several automated solutions have been proposed to aid IVUS segmentation, none has proven capable of processing IVUS images and accurately identifying the external elastic membrane (EEM) and lumen borders in real-time while the patient is in the cardiac catheterisation laboratory. [5]
Deep learning (DL) methodologies have begun to find medical application in facilitating the rapid and accurate processing of large imaging datasets. They rely on learning from large datasets of human annotations and use this information to train algorithmic models capable of processing images and replicating human performance within a few seconds. [6,7] These features render DL as the ideal approach for the analysis of IVUS images that have increased noise and artifacts which can confound conventional image-based segmentation approaches. Despite the undoubted theoretical advantages of DL methodologies in this setting, few studies have tested their potential value in IVUS analysis, mainly in small datasets providing promising results (Supplementary Table 1). [[8], [9], [10], [11], [12], [13], [14], [15], [16], [17]] The aim of this study is to develop and validate a DL-methodology capable of automatically and accurately segmenting IVUS data in real-time.
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Study population
Seventy patients who provided written, informed consent were recruited to the “Evaluation of the efficacy of computed tomographic coronary angiography in assessing coronary artery morphology and physiology” study (NCT03556644) and included in this analysis. The rationale and the study design have been presented in detail previously. [18] All patients had stable angina due to atherosclerosis detected on cardiac catheterisation and were referred to Barts Heart Centre for further invasive
Results
NIRS-IVUS was performed in 65 (197 vessels; 3.03 vessels/patient) out of 70 recruited patients; one patient was excluded due to acute kidney injury post CTCA, one patient was excluded due to findings of a thymoma on CTCA and three cases due to NIRS-IVUS catheter dysfunction. The baseline demographics of the studied patients and the output of the IVUS analysis are shown in Supplementary Table 2 and 3 respectively.
The training set consisted of 824,750 frames (177 vessels), of which 23,774
Discussion
In the present study we developed a novel DL-methodology and examined its efficacy in detecting the EEM and lumen borders in high-resolution IVUS images. The methodology was trained in a large dataset of 177 vessels and tested in 20 vessels. Both sets included frames portraying common artifacts seen in IVUS (guide-wire artifacts, non-uniform rotational distortion, motion artifacts, reverberations, side lobe artifacts and blood speckle artifacts) as well as frames with calcific tissue and side
Conclusions
In the present study we present for the first time a novel DL-methodology that is capable of accurate, automated segmentation of high-resolution IVUS images in real-time. An extensive morphological and quantitative validation in a large dataset has demonstrated that the proposed method is able to overcome common artifacts seen in IVUS and segment challenging frames portraying the origin of side branches and calcific plaques. These features are expected to facilitate its broad adoption and
Sources of funding
This study is jointly funded by British Heart Foundation (PG/17/18/32883), University College London Biomedical Research Centre (BRC492B) and Rosetrees Trust (A1773). RB, AR, VT, AM, AB and CVB are funded by Barts NIHR Biomedical Research Centre, London, UK.
Declaration of Competing Interest
All authors have no conflicts of interests to declare.
Acknowledgements
The authors wish to acknowledge the Cardiovascular Devices Hub at the Centre for Cardiovascular Medicine and Devices, Queen Mary University of London for supporting the present study.
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The 1st and 2nd author contributed equally to this work.