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Advanced atherosclerosis in the coronary arteries is one of the leadingcauses of deaths worldwide while being preventable and treatable. In order toimage atherosclerotic lesions (plaque), intravascular optical coherencetomography (IVOCT) can be used. The technique provides high-resolution imagesof arterial walls which allows for early plaque detection by experts. Due tothe vast amount of IVOCT images acquired in clinical routines, automatic plaquedetection has been addressed. For example, attenuation profiles in singleA-Scans of IVOCT images are examined to detect plaque. We address automaticplaque classification from entire IVOCT images, the cross-sectional view of theartery, using deep feature learning. In this way, we take context betweenA-Scans into account and we directly learn relevant features from the imagesource without the need for handcrafting features.

Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning