Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration.
Journal:
Medical physics
Published Date:
Mar 1, 2016
Abstract
PURPOSE: In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration.