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To analyze this characteristic of vulnerability, we developed an automateddeep learning method for detecting microvessels in intravascular opticalcoherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from85 lesions and 37 normal segments were analyzed. Manual annotation was doneusing a dedicated software (OCTOPUS) previously developed by our group. Dataaugmentation in the polar (r,{\theta}) domain was applied to raw IVOCT imagesto ensure that microvessels appear at all possible angles. Pre-processingmethods included guidewire/shadow detection, lumen segmentation, pixelshifting, and noise reduction. DeepLab v3+ was used to segment microvesselcandidates. A bounding box on each candidate was classified as eithermicrovessel or non-microvessel using a shallow convolutional neural network.For better classification, we used data augmentation (i.e., angle rotation) onbounding boxes with a microvessel during network training. Data augmentationand pre-processing steps improved microvessel segmentation performancesignificantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wisesensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifyingmicrovessels from candidates performed exceptionally well, with sensitivity of99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. Theclassification step eliminated the majority of residual false positives, andthe Dice coefficient increased from 0.71 to 0.73. In addition, our methodproduced 698 image frames with microvessels present, compared to 730 frommanual analysis, representing a 4.4% difference. When compared to the manualmethod, the automated method improved microvessel continuity, implying improvedsegmentation performance. The method will be useful for research purposes aswell as potential future treatment planning.

Automated segmentation of microvessels in intravascular OCT images using deep learning