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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20260522T150111Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post177@linklings.com
SUMMARY:Convolutional Neural Networks for Coronary Plaque Classification i
 n Intravascular Optical Coherence Tomography (IVOCT) Images
DESCRIPTION:Chaitanya Kolluru, David Prabhu, Yanzan Gharaibeh, David Wilso
 n, and Sanjaya Gajurel (Case Western Reserve University)\n\nCurrently, IVO
 CT is the only imaging technique with the resolution necessary to identify
  vulnerable thin cap fibro-atheromas (TCFAs). IVOCT also has greater penet
 ration depth in calcified plaques as compared to Intravascular Ultrasound 
 (IVUS). Despite its advantages, IVOCT image interpretation is challenging 
 and time consuming with over 500 images generated in a single pullback. In
  this poster, we propose a method to automatically classify A-lines in IVO
 CT images using a convolutional neural network. Conditional random fields 
 were used to clean network predictions across frames. The neural network w
 as trained using a dataset of nearly 4,500 image frames across 48 IVOCT pu
 llbacks. Ten-fold cross validation with held-out pullbacks resulted in a c
 lassification accuracy of roughly 76% for fibrocalcific, 84% for fibrolipi
 dic, and 85% for other. Classification results across frames displayed in 
 en face view matched closely to annotated counterparts.\n\nRegistration Ca
 tegory: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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