Effective user interaction in online interactive semantic segmentation of glioblastoma magnetic resonance imaging.

ABSTRACT: Interactive segmentation is a promising approach to solving the pervasive shortage of reference annotations for automated medical image processing. We focus on the challenging task of glioblastoma segmentation in magnetic resonance imaging using a random forest pixel classifier trained iteratively on scribble annotations. Our experiments use data from the MICCAI Multimodal Brain Tumor Segmentation Challenge 2013 and simulate expert interactions using different approaches: corrective annotations, class-balanced corrections, annotations where classifier uncertainty is high, and corrections where classifier uncertainty is high/low. We find that it is better to correct the classifier than to provide annotations where the classifier is uncertain, resulting in significantly better Dice scores in

SUBMITTER: Petersen J 

PROVIDER: S-EPMC5566286 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature