Deformable registration using shape statistics with applications in sinus surgery

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Johns Hopkins University
Evaluating anatomical variations in structures like the nasal passage and sinuses is challenging because their complexity can often make it difficult to differentiate normal and abnormal anatomy. By statistically modeling these variations and estimating individual patient anatomy using these models, quantitative estimates of similarity or dissimilarity between the patient and the sample population can be made. In order to do this, a spatial alignment, or registration, between patient anatomy and the statistical model must first be computed. In this dissertation, a deformable most likely point paradigm is introduced that incorporates statistical variations into probabilistic feature-based registration algorithms. This paradigm is a variant of the most likely point paradigm, which incorporates feature uncertainty into the registration process. The deformable registration algorithms optimize the probability of feature alignment as well as the probability of model deformation allowing statistical models of anatomy to estimate, for instance, structures seen in endoscopic video without the need for patient specific computed tomography (CT) scans. The probabilistic framework also enables the algorithms to assess the quality of registrations produced, allowing users to know when an alignment can be trusted. This dissertation covers three algorithms built within this paradigm and evaluated in simulation and in-vivo experiments.
deformable registration, statistical shape models, video-CT registration, nasal cavity, sinus cavity