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IV\u2010OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in\u2010stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV\u2010OCT images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p>The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV\u2010OCT readers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p>Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case\u2010by\u2010case validation also showed comparable accuracy for most cases. High correlation coefficients (<jats:italic>R<\/jats:italic> &gt; 0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 <jats:italic>\u03bc<\/jats:italic>m, respectively) and a narrow range of limits of agreement (36 and 54 <jats:italic>\u03bc<\/jats:italic>m, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions:<\/jats:title><jats:p>The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent\u2010related side effects, including in\u2010stent thrombosis and in\u2010stent restenosis.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.4943374","type":"journal-article","created":{"date-parts":[[2016,3,14]],"date-time":"2016-03-14T17:00:42Z","timestamp":1457974842000},"page":"1662-1675","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":54,"title":["Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage"],"prefix":"10.1002","volume":"43","author":[{"given":"Hyeong Soo","family":"Nam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang\u2010Soo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Joong","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon Woo","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Won","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongki","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2016,3,14]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1957169"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.19.7.071412"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/archopht.1995.01100030081025"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0161\u20106420(95)31032\u20109"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1034\/j.1600\u20100846.2001.007001001.x"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdermsci.2005.07.006"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/nm1450"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.276.5321.2037"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2008.06.007"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13534\u2010014\u20100151\u20101"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.104.523563"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM198010163031601"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0735\u20101097(97)00450\u20106"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.293.17.2126"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehn553"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0735\u20101097(02)02374\u20104"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.106.675934"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.107.693739"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1136\/heart.89.3.317"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2009.10.006"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehl413"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2010.02.008"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1136\/hrt.2007.118679"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2012.01.021"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/ccd.24871"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/53\/12\/001"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2037755"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10554\u2010011\u20109824\u20103"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/56\/20\/010"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.3.002809"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10554\u2010012\u20100064\u2010y"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4803461"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0004\u20103702(97)00063\u20105"},{"key":"e_1_2_8_35_1","volume-title":"Neural Networks and Learning Machines","author":"Haykin S. 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