Automatic microchannel detection using deep learning in intravascular optical coherence tomography images

We developed a new method for automated detection of microchannel in intravascular optical coherence tomography images. The proposed method includes three main steps including pre-processing, identification of microchannel candidates, and classification of microchannel. Our method provided excellent segmentation of microchannel with a Dice coefficient of 0.811, sensitivity of 92.4%, and specificity of 99.9%. Our method has great potential to enable highly automated, objective, repeatable, and comprehensive evaluations of vulnerable plaques and treatments. We believe that this method is promising for both research and clinical applications.

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