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Towards automated dynamic scene analysis and augmentation during image-guided radiological and surgical interventions

University of British Columbia

This thesis proposes non-invasive automated scene analysis and augmentation techniques to improve navigation in image-guided therapy (IGT) applications. IGT refers to procedures in which physicians rely on medical images to plan, perform, and monitor an intervention. In IGT, the tomographic images acquired before the intervention may not directly correspond to what the physician sees via the intraoperative imaging. This is due to many factors such as: time-varying changes in the patient's anatomy (e.g., patient positioning or changes in pathology), risk of overexposure to ionizing radiation (restricted use of X-ray imaging), operational costs, and differences in imaging modalities. This inconsistency often results in a navigational problem that demands substantial additional effort from the physician to piece together a mental representation of complex correspondences between the preoperative images and the intraoperative scene. The first direction explored in this thesis, investigates the application of image-based motion analysis techniques for vessel segmentation. Specifically, we propose novel motion-based segmentation methods to enable safe, fast, and automatic localization of vascular structures from dynamic medical image sequences and demonstrated their efficacy in segmenting vasculature from surgical video and dynamic medical ultrasound sequences. The second direction investigates ways in which navigation uncertainties can be computed, propagated, and visualized in the context of IGT navigation systems that target deformable soft-tissues. Specifically, we present an uncertainty-encoded scene augmentation method for robot-assisted laparoscopic surgery, in which we propose visualization techniques for presenting probabilistic tumor margins. We further present a computationally efficient framework to estimate the uncertainty in deformable image registration and to subsequently propagate the effects of the computed uncertainties through to the visualizations, organ segmentations, and dosimetric evaluations performed in the context of fractionated image-guided brachytherapy. Our contributions constitute a step towards automated and real-time IGT navigation and may, in the near future, help to improve interventional outcomes for patients (improved targeting of pathologies) and increase surgical efficiency (less effort required by the physician).

Text

2017-12-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/64035

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/