A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
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 segmentationdoi:10.1109/isbi.2016.7493299 dblp:conf/isbi/AlbertsRAHKZM16 fatcat:ylvjuxdom5bhnhobeuy7xawe6m