Computer‐aided detection of intracoronary stent in intravascular ultrasound sequences. Issue 10 (23rd September 2016)

• Record Type: Journal Article
• Authors: Ciompi, Francesco; Balocco, Simone; Rigla, Juan; Carrillo, Xavier; Mauri, Josepa; Radeva, Petia
• Is Part Of: Medical physics. Volume 43:Issue 10(2016)
• Page Start: 5616
• Page End: 5625
• Publication Date: 2016-09-23
• Subjects: biomedical ultrasonics -- blood vessels -- image sequences -- learning (artificial intelligence) -- medical image processing -- stents; Ultrasonographic imaging; Diagnosis using ultrasonic, sonic or infrasonic waves -- Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; IVUS -- Stent detection -- coronary arteries; Intravascular ultrasonography -- Optical coherence tomography -- Bifurcations -- Sequence analysis -- Calcium -- Image analysis -- Vascular system -- Image detection systems; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4962927
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during percutaneous coronary intervention (PCI), in order to prevent acute vessel occlusion. The identification of struts location and the definition of the stent shape is relevant for PCI planning and for patient follow‐up. The authors present a fully automatic framework for computer‐aided detection (CAD) of intracoronary stents in intravascular ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape. Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classification. The output of the classification stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multicentric data‐set of 1, 015 images from 107 IVUS sequences containing both metallic and bioabsorbable stents. Results: The method was able to detect struts in both metallic stents with an overall F‐measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bioabsorbable stents with an overall F‐measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts. Conclusions: The results are close to the interobserver variability and suggest that the system has the potential of being used as a method for aiding percutaneous interventions.