Development of an innovative technology to segment luminal borders of intravascular ultrasound image sequences in a fully automated manner

Abouzar Moshfegh, Ashkan Javadzadegan, Maryam Mohammadi, Lakshitha Ravipudi, Shaokoon Cheng, Ralph Martins

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Although intravascular ultrasound (IVUS) is the commonest intravascular imaging modality, it still is inefficient for clinical use as it requires laborious manual analysis. This study demonstrates the feasibility of a near real-time fully automated technology for accurate identification, detection, and quantification of luminal borders in intravascular images. This technology uses a combination of the novel approaches of a self-tuning engine, dynamic and static masking systems, radar-wise scan, and contour correction cycle method. The performance of the computer algorithm developed based on this technology was tested on a sequence of IVUS and True Vessel Characterization (TVC) images obtained from the left anterior descending (LAD) artery of 6 patients with coronary artery disease. The accuracy of the algorithm was evaluated by comparing luminal borders traced manually with those detected automatically. The processing time of the developed algorithm was also tested on a Dell laptop with an Intel Core i7-8750H Processor (4.1 GHz with 6 cores, 9 MB Cache). Linear regression and Bland-Altman analyses indicated high correlation between manual and automatic tracings (Y = 0.80 × X+1.70, R2 = 0.88 & 0.67 ± 1.31 (bias±SD)). Whereas analysis of 2000 IVUS images using one CPU core with a 30% load took 23.12 min, the same analysis using six CPU cores with 90% load took 1.0 min. The performance, accuracy, and speed of the presented state-of-the-art technology demonstrates its capacity for use in clinical settings.
Original languageEnglish
Pages (from-to)111-121
Number of pages11
JournalComputers in Biology and Medicine
Volume108
DOIs
Publication statusPublished - May 2019

Keywords

  • intravascular imaging
  • Real-time lumen detection
  • Machine learning
  • Fully automated analysis

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