By Ji Yang, Lin Tong, Mehdi Faraji and Anup Basu.
Multimedia Research Centre, Department of Computing Science, University of Alberta.
This repository contains the original models and code described in the paper "IVUS-Net: An Intravascular Ultrasound Segmentation Network" (https://arxiv.org/abs/1806.03583).
IntraVascular UltraSound (IVUS) is one of the most effective imaging modalities that provides assistance to experts in order to diagnose and treat cardiovascular diseases. We address a central problem in IVUS image analysis with Fully Convolutional Network (FCN): automatically delineate the lumen and media-adventitia borders in IVUS images, which is crucial to shorten the diagnosis process or benefits a faster and more accurate 3D reconstruction of the artery. Particularly, we propose an FCN architecture, called IVUS-Net, followed by a post-processing contour extraction step, in order to automatically segments the interior (lumen) and exterior (media-adventitia) regions of the human arteries. The proposed work, to the best of our knowledge, is the first deep learning based method for segmentation of both the lumen and the media vessel walls in 20MHz IVUS B-mode images that achieves the best results without any manual intervention.
@article{yang2018ivus,
author = {Ji Yang and Lin Tong and Mehdi Faraji and Anup Basu},
title = {IVUS-Net: An Intravascular Ultrasound Segmentation Network},
journal = {arXiv preprint arXiv:1806.03583},
year = {2018}
}