Uncertainty quantification in brain tumor segmentation using CRFs and random perturbation models

Esther Alberts, Markus Rempfler, Georgina Alber, Thomas Huber, Jan Kirschke, Claus Zimmer, Bjoern H. Menze
2016 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)  
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
more » ... ertainties 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 conditional random field (CRF) followed by maximum a posteriori (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. Index Terms-Random MAP perturbations, conditional random fields, uncertainty quantification, medical image segmentation
doi:10.1109/isbi.2016.7493299 dblp:conf/isbi/AlbertsRAHKZM16 fatcat:ylvjuxdom5bhnhobeuy7xawe6m