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Lumen and vessel wall segmentation on intravascular ultrasound images using fully convolutional network

In this study, we performed deep learning analysis for the automatic segmentation of vessel and lumen in intravascular ultrasound (IVUS) images. Extracting vascular boundaries from intravascular ultrasound images are essential for the quantitative analysis of cardiovascular diseases. We applied a fully convolutional network (FCN) based semantic segmentation technique and transfer learning. To consider the continuity of the IVUS images, we filled in RGB channels with the central image and the nearby images with displacement and trained different FCN model for each displacement. Based on our experiments, we obtained 0.97 ± 0.03 of dice similarity coefficient (DSC) value in the vessel and 0.91 ± 0.09 of DSC value in the lumen. Due to their robustness and accuracy, this method is highly promising to be used in clinical practice.