3-D Stent Detection in Intravascular OCT Using a Bayesian Network and Graph Search.
3-D Stent Detection in Intravascular OCT Using a Bayesian Network and Graph Search.
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DOI：
10.1109/tmi.2015.2405341
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发表时间：
2015-07
影响因子：
10.6
通讯作者：
Rollins AM
中科院分区：
文献类型：
--
作者：
Zhao Wang;Jenkins MW;Linderman GC;Bezerra HG;Fujino Y;Costa MA;Wilson DL;Rollins AM
Worldwide, many hundreds of thousands of stents are implanted each year to revascularize occlusions in coronary arteries. Intravascular optical coherence tomography (OCT) is an important emerging imaging technique, which has the resolution and contrast necessary to quantitatively analyze stent deployment and tissue coverage following stent implantation. Automation is needed, as current, it takes up to 16 hours to manually analyze hundreds of images and thousands of stent struts from a single pullback. For automated strut detection, we used image formation physics and machine learning via a Bayesian network, and 3-D knowledge of stent structure via graph search. Graph search was done on en face projections using minimum spanning tree algorithms. Depths of all struts in a pullback were simultaneously determined using graph cut. To assess the method, we employed the largest validation data set used so far, involving more than 8,000 clinical images from 103 pullbacks from 72 patients. Automated strut detection achieved a 0.91±0.04 recall, and 0.84±0.08 precision. Performance was robust in images of varying quality. This method can improve the workflow for analysis of stent clinical trial data, and can potentially be used in the clinic to facilitate real-time stent analysis and visualization, aiding stent implantation.
