Abstract
Intravascular optical coherence tomography (IVOCT) is used to assess stent tissue coverage and malapposition in stent evaluation trials. We developed the OCT Image Visualization and Analysis Toolkit for Stent (OCTivat-Stent), for highly automated analysis of IVOCT pullbacks. Algorithms automatically detected the guidewire, lumen boundary, and stent struts; determined the presence of tissue coverage for each strut; and estimated the stent contour for comparison of stent and lumen area. Strut-level tissue thickness, tissue coverage area, and malapposition area were automatically quantified. The software was used to analyze 292 stent pullbacks. The concordance-correlation-coefficients of automatically measured stent and lumen areas and independent manual measurements were 0.97 and 0.99, respectively. Eleven percent of struts were missed by the software and some artifacts were miscalled as struts giving 1% false-positive strut detection. Eighty-two percent of uncovered struts and 99% of covered struts were labeled correctly, as compared to manual analysis. Using the highly automated software, analysis was harmonized, leading to a reduction of inter-observer variability by 30%. With software assistance, analysis time for a full stent analysis was reduced to less than 30 minutes. Application of this software to stent evaluation trials should enable faster, more reliable analysis with improved statistical power for comparing designs.
Highlights
OPEN Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence
We developed the OCT Image Visualization and Analysis Toolkit for Stent (OCTivat-Stent), for highly automated analysis of Intravascular optical coherence tomography (IVOCT) pullbacks
Eleven percent of struts were missed by the software and some artifacts were miscalled as struts giving 1% false-positive strut detection
Summary
OPEN Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence. Ughi et al proposed an automated analysis method for measuring distance between the lumen and detected strut[20] They acquired IVOCT images from rabbit iliac arteries, and showed good correlations of coverage quantification among the automatic analysis, manual analysis, and histological assessment. They reported that only using distances between struts and lumen boundary is not sufficient for differentiating thinly covered and uncovered struts. Our group proposed the fully-automated stent coverage analysis method to quantitatively evaluate the stent tissue coverage[27] This method enabled to classify covered and uncovered struts, measure tissue thickness, and determine clusters of uncovered struts. In order to avoid the potential inter-observer variability, we performed active learning relabeling for all pullbacks
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
