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Presentation Information     2015-12-17 (15:15)   •  The seminar room at Vi2

Speaker Alexandre Xavier Falc„o  (CBA)
Comment Institute of Computing, University of Campinas
Type External presentation
Title Medical Image Segmentation using Object Shape Models: A Critical Review on Recent Trends, and Alternative Directions
Abstract Segmentation is important to define the spatial extension of body anatomic structures (objects) in medical images for quantitative analysis. In this context, it is desirable to eliminate (at least minimize) user interaction. This aim is feasible by combining object delineation algorithms with Object Shape Models (OSMs). While the former can better capture the actual shape of the object in the image, the latter provides shape constraints to assist its location and delineation. In this talk, we will review two important classes of OSMs for medical image segmentation: statistical (SOSMs) and fuzzy (FOSMs). SOSMs rely on the image mapping onto a reference coordinate system, which indicates the probability of each voxel to be in the object (a probabilistic atlas built from a set of training images and their segmentation masks). Imperfect mappings due to shape and texture variations asks for object delineation algorithms, but the methods usually assume that the atlas is at the best position for delineation. Multiple atlases per object can mitigate the problem and a recent trend is to use each training mask as an individual atlas. By mapping them onto the coordinate system of a new image, object delineation can be accomplished by label fusion. However, the processing time for deformable registration is critical to make SOSMs suitable for large scale studies. FOSMs appear as a recent alternative to avoid reference systems (deformable registration) by translating the training masks to a common reference point for model construction. This relaxes the shape constraints, but asks for a more effective object delineation algorithm and some efficient approach for object's location. One of the solutions, named optimum object search, translates the model inside an estimated search region in the image while a criterion function guides translation and determines the best delineated object among candidates. This makes segmentation with FOSMs considerably faster than with SOSMs, but SOSMs that adopt the optimum object search can be more effective and with less atlases per object. We will then discuss the pros and cons of the recent FOSM and SOSM approaches by providing alternative directions, which also include the user to correct segmentation errors and improve the models.