Machine Stereo Vision for Medical Image Registration

Image guided liver surgery aims to enhance the precision of resection and ablation by providing fast localization of tumours and adjacent complex vasculature to improve oncologic outcome. This dissertation presents a novel end-to-end system for fast and accurate 3D surface reconstruction and motion estimation of the liver for alignment of intraoperative imagery with a preoperative volumetric scan. The system is designed and evaluated for application to liver surgery in an open setting, where open surgery is the dominant setting. The system is comprised of three key components: initialization, 3D surface recovery, and 3D motion estimation. Initialization is performed semi-automatically using a Branch-and-Bound (BnB) strategy to generate a set of globally optimal shape-based registration candidates from which the user can select a suitable initialization. 3D surface recovery is performed using a computationally efficient adaptive Coarse-to-Fine (CTF) stereo algorithm providing data-driven dense reconstructions in a computationally-efficient manner. A robust, 3D motion estimation technique based on interframe feature matching is then used to register a time series of reconstructions back to the initial frame of the sequence. The system has been evaluated empirically with reference to novel laboratory and intraoperative datasets, with results showing that performance is within tolerances expected for integration into Surgical Navigation (SN) systems.