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21 March 2016 Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations
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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 variability 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 the template to the patient image. We use 3D snake splines to correct errors in this initial segmentation. Once we have several accurately segmented images, we build statistical shape models to observe the population mean and variance for each structure. These shape models are useful to us in several ways. Regular registration methods are insufficient to accurately register pre-operative computed tomography (CT) images with intra-operative endoscopy video of the sinuses. This is because of deformations that occur in structures containing 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 shapes and variances in different populations, such as different genders or ethnicities, in order to observe differences and similarities, as well as in different age groups in order to observe the developmental changes that occur in the sinuses.
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Ayushi Sinha, Simon Leonard, Austin Reiter, Masaru Ishii, Russell H. Taylor, and Gregory D. Hager "Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840D (21 March 2016);

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