Automatic microchannel detection using deep learning in intravascular optical coherence tomography images
Juhwan Lee, Justin N. Kim, Gabriel Tensol Rodrigues Pereira, Yazan Gharaibeh, Chaitanya Kolluru, Vladislav N. Zimin, Luís Augusto Palma Dallan, Issam Motairek, Ammar Hoori, Giulio Guagliumi, Hiram G. Bezerra, David L. Wilson
Abstract
) IVOCT image. We used the DeepLab-v3 plus deep learning model with the Xception backbone network for identifying microchannel candidates. After microchannel candidate detection, each candidate was classified as either microchannel or no-microchannel using a convolutional neural network (CNN) classification model. Our method provided excellent segmentation of microchannel with a Dice coefficient of 0.811, sensitivity of 92.4%, and specificity of 99.9%. We found that pre-processing and data augmentation were very important to improve results. In addition, a CNN classification step was also helpful to rule out false positives. Furthermore, automated analysis missed only 3% of frames having microchannels and showed no false positives. 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.