Intravascular Optical Coherence Tomography (IVOCT) is a high contrast, 3D microscopic imaging technique that can be
used to assess atherosclerosis and guide stent interventions. Despite its advantages, IVOCT image interpretation is
challenging and time consuming with over 500 image frames generated in a single pullback volume. We have developed
a method to classify voxel plaque types in IVOCT images using machine learning. To train and test the classifier, we
have used our unique database of labeled cadaver vessel IVOCT images accurately registered to gold standard cryoimages.
This database currently contains 300 images and is growing. Each voxel is labeled as fibrotic, lipid-rich,
calcified or other. Optical attenuation, intensity and texture features were extracted for each voxel and were used to build
a decision tree classifier for multi-class classification. Five-fold cross-validation across images gave accuracies of 96 %
± 0.01 %, 90 ± 0.02% and 90 % ± 0.01 % for fibrotic, lipid-rich and calcified classes respectively. To rectify
performance degradation seen in left out vessel specimens as opposed to left out images, we are adding data and
reducing features to limit overfitting. Following spatial noise cleaning, important vascular regions were unambiguous in
display. We developed displays that enable physicians to make rapid determination of calcified and lipid regions. This
will inform treatment decisions such as the need for devices (e.g., atherectomy or scoring balloon in the case of
calcifications) or extended stent lengths to ensure coverage of lipid regions prone to injury at the edge of a stent.