A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images

Amin Katouzian, Elsa D. Angelini, Stéphane G. Carlier, Jasjit S. Suri, Nassir Navab, Andrew F. Laine

Research output: Contribution to journalArticlepeer-review

125 Scopus citations

Abstract

Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.

Original languageEnglish
Article number6159086
Pages (from-to)823-834
Number of pages12
JournalIEEE Transactions on Information Technology in Biomedicine
Volume16
Issue number5
DOIs
StatePublished - 2012

Keywords

  • Intravascular ultrasound (IVUS)
  • lumen
  • media-adventitia (MA)
  • segmentation

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