Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations

We present an automatic segmentation and statistical shape modeling system for the paranasal sinuses which allows us to locate structures in and around the sinuses, as well as to observe the natural variations that occur in these structures. This system involves deformably registering a given patient image to a manually segmented template image, and using the resulting deformation field to transfer labels from template to patient. We use 3D snake splines to correct errors in the deformable registration. Once we have several accurately segmented images, we build statistical shape models for each structure in the sinus allowing us to observe the mean shape of the population, as well as the variations observed in the population. These shape models are useful in several ways. First, regular video-CT registration methods are insufficient to accurately register pre-operative computed tomography (CT) images with intra-operative endoscopy video because of deformations that occur in structures containing high amounts of erectile tissue. Our aim is to estimate these deformations using our shape models in order to improve video-CT registration, as well as to distinguish normal variations in anatomy from abnormal variations, and automatically detect and stage pathology. We can also compare the mean shape and variances of different populations, such as different genders or ethnicities, and observe the differences and similarities, as well as of different age groups, and observe the developmental changes that occur in the sinuses.