title: Incorporating User Input in Template-Based Segmentation.
abstract: We present a simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs. The user provides a partial segmentation of the organ of interest, which is used to guide the template towards its target. The user also highlights some elements of the background that should be excluded from the final segmentation. We derive by likelihood maximization a registration algorithm from a simple statistical image model in which the user labels are modeled as Bernoulli random variables. The resulting registration algorithm minimizes the sum of square differences between the binary template and the user labels, while preventing the template from shrinking, and penalizing for the inclusion of background elements into the final segmentation. We assess the performance of the proposed algorithm on synthetic images in which the amount of user annotation is controlled. We demonstrate our algorithm on the segmentation of the lungs ofMycobacterium tuberculosisinfected mice fromμCT images.
authors.affiliation: Division of Imaging and Applied Mathematics, CDRH, Food and Drug Administration.
authors.forename: Camille
authors.initials: C
authors.lastname: Vidal
authors.affiliation: Dept. of Applied Mathematics and Statistics, Johns Hopkins University.
authors.forename: Dale
authors.initials: D
authors.lastname: Beggs
authors.affiliation: Center for Imaging Science, Johns Hopkins University ; Dept. of Applied Mathematics and Statistics, Johns Hopkins University.
authors.forename: Laurent
authors.initials: L
authors.lastname: Younes
authors.affiliation: Center for Tuberculosis Research, Johns Hopkins University School of Medicine.
authors.forename: Sanjay K
authors.initials: SK
authors.lastname: Jain
authors.affiliation: Center for Imaging Science, Johns Hopkins University ; Dept. of Applied Mathematics and Statistics, Johns Hopkins University.
authors.forename: Bruno
authors.initials: B
authors.lastname: Jedynak
keywords: Diseased Organs; Registration; Template-based Segmentation; User Input