Preoperative Surgical Planning

Abstract

Since several decades, minimally-invasive surgery has continuously improved both clinical workflow and outcome. Such procedures minimize patient trauma, decrease hospital stay or reduce risk of infection. Next generation robot-assisted interventions promise to further improve on these advantages while at the same time opening the way to new surgical applications. Temporal Bone Surgery and Endovascular Aortic Repair are two examples for such currently researched approaches, where manual insertion of instruments, subject to a clinician’s experience and daily performance, could be replaced by a robotic procedure. In the first, a flexible robot would drill a nonlinear canal through the mastoid, allowing a surgeon access to the temporal bone’s apex, a target often unreachable without damaging critical risk structures. For the second example, robotically driven guidewires could significantly reduce the radiation exposure from fluoroscopy, that is exposed to patients and surgeons during navigation through the aorta. These robot-assisted surgeries require preoperative planning consisting of segmentation of risk structures and computation of nonlinear trajectories for the instruments. While surgeons could so far rely on preoperative images and a mental 3D model of the anatomy, these new procedures will make computational assistance inevitable due to the added complexity from image processing and motion planning. The automation of tiresome and manually laborious tasks is therefore crucial for successful clinical implementation. This thesis addresses these issues and presents a preoperative pipeline based on CT images that automates segmentation and trajectory planning. Major contributions include an automatic shape regularized segmentation approach for coherent anatomy extraction as well as an exhaustive trajectory planning step on locally optimized Bézier Splines. It also introduces thorough in silico experiments that perform functional evaluation on real and synthetically enlarged datasets. The benefits of the approach are shown on an in house dataset of otobasis CT scans as well as on two publicly available datasets containing aorta and heart.