Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images. (15th September 2021)

• Record Type: Journal Article
• Authors: Bajaj, Retesh; Huang, Xingru; Kilic, Yakup; Ramasamy, Anantharaman; Jain, Ajay; Ozkor, Mick; Tufaro, Vincenzo; Safi, Hannah; Erdogan, Emrah; Serruys, Patrick W.; Moon, James; Pugliese, Francesca; Mathur, Anthony; Torii, Ryo; Baumbach, Andreas; Dijkstra, Jouke; Zhang, Qianni; Bourantas, Christos V.
• Is Part Of: International journal of cardiology. Volume 339(2021)
• Page Start: 185
• Page End: 191
• Publication Date: 2021-09-15
• Subjects: Intravascular ultrasound -- Image segmentation -- Machine learning; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2021.06.030
• Languages: English
• ISSNs: 0167-5273

Abstract

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 ≤0.23mm 2 (standard deviation ≤0.85mm 2 ), 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. Highlights: Novel machine learning methodology for IVUS segmentation trained and tested in the largest dataset to date. The methodology introduced reliably matches human experts at segmenting IVUS images but is an order of magnitude faster. In user-friendly software the methodology is expected to enable real-time IVUS analysis and optimal treatment planning.