Deformable registration using shape statistics with applications in sinus surgery

Ayushi Sinha, 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 model must first be computed. In this dissertation, a deformable most likely point paradigm is introduced that incorporates statistical variations into feature-based registration algorithms. This paradigm is a variant of the most likely point paradigm, which incorporates feature uncertainty into the registration process. Our 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 talk will cover 3 algorithms built within this paradigm and evaluated in simulation and in-vivo experiments.

Speaker Biography

Ayushi is a PhD candidate in Computer Science at JHU under the supervision of Dr. Russ Taylor and Dr. Greg Hager. She received her B.S in Computer Science and B.A. in Mathematics from Providence College, RI in 2011. During the course of her PhD, she worked on improving statistical shape models of anatomy and on using these models in deformable registration techniques. After finishing her PhD, Ayushi plans to continue developing these ideas further as a Provost Postdoctoral Fellow at JHU.