Virtual Blood Vessels in Complex Background using Stereo X-ray Images

TL;DR

This paper presents an automatic system for reconstructing and visualizing 3D blood vessels from stereo X-ray images in AR, reducing radiation exposure and improving medical visualization.

Contribution

It introduces a novel pipeline combining segmentation, centerline extraction, and a coarse-to-fine stereo matching scheme for vessel reconstruction in AR.

Findings

Achieved accurate vessel reconstruction on synthetic and real data.

Demonstrated effective AR visualization with HoloLens.

Reduced radiation exposure by using only two X-ray images.

Abstract

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.