TY  - JOUR
AU  - Francesco Ciompi
AU  - Simone Balocco
AU  - Juan Rigla
AU  - Xavier Carrillo
AU  - Josefina Mauri
AU  - Petia Radeva
PY  - 2016//
TI  - Computer-Aided Detection of Intra-Coronary Stent in Intravascular Ultrasound Sequences
T2  - MP
JO  - Medical Physics
VL  - 43
IS  - 10
N2  - 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 identication of struts location and the denition of the stent shape are relevant for PCI planning 15 and for patient follow-up. We present a fully-automatic framework for Computer-Aided Detection(CAD) of intra-coronary 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 classication. The output of the classication 20 stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multi-centric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bio-absorbable stents.Results: The method was able to detect structs 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 bio-absorbable 25 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 inter-observer variability and suggest that the system has the potential of being used as method for aiding percutaneous interventions.
UR  - http://dx.doi.org/10.1118/1.4962927
N1  - MILAB
ID  - Francesco Ciompi2016
ER  -