A State-Of-The-Art Review on Coronary Artery Border Segmentation Algorithms for Intravascular Ultrasound (IVUS) Images

© 2023. The Author(s) under exclusive licence to Biomedical Engineering Society..

Intravascular Ultrasound images (IVUS) is a useful guide for medical practitioners to identify the vascular status of coronary arteries in human beings. IVUS is a unique intracoronary imaging modality that is used as an adjunct to angioplasty to view vessel structures using a catheter with high resolutions. Segmentation of IVUS images has always remained a challenging task due to various impediments, for example, similar tissue components, vessel structures, and artifacts imposed during the acquisition process. Many researchers have applied various techniques to develop standard methods of image interpretation, however, the ultimate goal is still elusive to most researchers. This challenge was presented at the MICCAI- Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop in 2011. This paper presents a major review of recently reported work in the field, with a detailed analysis of various segmentation techniques applied in IVUS, and highlights the directions for future research. The findings recommend a reference database with a larger number of samples acquired at varied transducer frequencies with special consideration towards complex lesions, suitable validation metrics, and ground-truth definition as a standard against which to compare new and current algorithms.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Cardiovascular engineering and technology - 14(2023), 2 vom: 17. Apr., Seite 264-295

Sprache:

Englisch

Beteiligte Personen:

Arora, Priyanka [VerfasserIn]
Singh, Parminder [VerfasserIn]
Girdhar, Akshay [VerfasserIn]
Vijayvergiya, Rajesh [VerfasserIn]

Links:

Volltext

Themen:

Image segmentation
Intravascular ultrasound (IVUS)
Journal Article
Lumen
Media- adventitia (MA)
Review

Anmerkungen:

Date Completed 17.04.2023

Date Revised 28.04.2023

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1007/s13239-023-00654-6

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM351611320