PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

E. Alberts, M. Rempfler, G. Alber, M. Huber, J. Kirschke, C. Zimmer, B. Menze
Uncertainty quantification in brain tumor segmentation using CRFs and random perturbation models
IEEE International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, April 2016 (bib)

Medical image segmentation is a challenging task and algorithms often struggle with the high variability of inhomogeneous clinical data, demanding different parameter settings or resulting in weak segmentation accuracy across different inputs. Assessing the uncertainty in the resulting segmentation therefore becomes crucial for both communicating with the end-user and calculating further metrics of interest based on it, for example, in tumor volumetry. In this paper, we quantify segmentation uncertainties in a energy minimisation method where computing probabilistic segmentations is non-trivial. We follow recently proposed work on random perturbation models that enables us to sample segmentations efficiently by repeatedly perturbing the energy function of the CRF followed by MAP inference. We conduct experiments on brain tumor segmentation, with both voxel and supervoxel perturbations, and demonstrate the benefits of probabilistic segmentations by means of precision-recall curves and uncertainties in tumor volumetry along time.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif