Virtual Blood Vessels in Complex Background using Stereo X-ray Images
This addresses the need for safer and cheaper 3D medical imaging by reducing irradiation exposure, though it is incremental as it builds on existing segmentation and matching techniques.
The authors tackled the problem of reconstructing 3D blood vessels from stereo X-ray images with bones and body fat, achieving satisfactory quantitative and qualitative results in evaluations using synthetic and real images.
We propose a fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat. Currently, typical 3D imaging technologies are expensive and carrying the risk of irradiation exposure. To reduce the potential harm, we only need to take two X-ray images before visualizing the vessels. Our system can effectively reconstruct and visualize vessels in following steps. We first conduct initial segmentation using Markov Random Field and then refine segmentation in an entropy based post-process. We parse the segmented vessels by extracting their centerlines and generating trees. We propose a coarse-to-fine scheme for stereo matching, including initial matching using affine transform and dense matching using Hungarian algorithm guided by Gaussian regression. Finally, we render and visualize the reconstructed model in a HoloLens based AR system, which can essentially change the way of visualizing medical data. We have evaluated its performance by using synthetic and real stereo X-ray images, and achieved satisfactory quantitative and qualitative results.