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Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images

Bajaj, R; Huang, X; Kilic, Y; Ramasamy, A; Jain, A; Ozkor, M; Tufaro, V; ... Bourantas, CV; + view all (2021) Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images. International Journal of Cardiology , 339 pp. 185-191. 10.1016/j.ijcard.2021.06.030. Green open access

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Abstract

AIMS: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. METHODS AND RESULTS: IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754-1.061) with similar results in frames portraying calcific plaques or side branches. CONCLUSIONS: The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research.

Type: Article
Title: Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ijcard.2021.06.030
Publisher version: https://doi.org/10.1016/j.ijcard.2021.06.030
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Image segmentation, Intravascular ultrasound, Machine learning, Coronary Vessels, Deep Learning, Humans, Plaque, Atherosclerotic, Ultrasonography, Ultrasonography, Interventional
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Clinical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10137396
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