title: Evaluating segmentation error without ground truth.
abstract: The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems. In this paper we present a generic learning approach based on a novel space of segmentation features, which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation. We show the regressor to be much stronger a predictor of these error metrics than the responses of probabilistic boosting classifiers trained on the segmentation boundary. The presented approach not only allows us to build reliable confidence measures and fidelity checks, but also to rank several segmentation hypotheses against each other during online usage of the segmentation algorithm in clinical practice.
authors.affiliation: Imaging and Computer Vision, Siemens Corp., Corporate Research and Technology, Princeton, NJ, USA.
authors.forename: Timo
authors.initials: T
authors.lastname: Kohlberger
authors.forename: Vivek
authors.initials: V
authors.lastname: Singh
authors.forename: Chris
authors.initials: C
authors.lastname: Alvino
authors.forename: Claus
authors.initials: C
authors.lastname: Bahlmann
authors.forename: Leo
authors.initials: L
authors.lastname: Grady
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000473:pathology*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D011336:Probability; D015203:Reproducibility of Results