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 the edema (0.662 to 0.686) and necrosis (0.550 to 0.676) regions after 20 interactions. It is also advantageous to balance inputs among classes, with significantly better Dice in the necrotic (0.501 to 0.676) and nonenhancing (0.151 to 0.235) regions compared to fully random corrections. Corrective annotations in regions of high classifier uncertainty provide no additional benefit, low uncertainty corrections perform worst. Preliminary experiments with real users indicate that those with intermediate proficiency make a considerable number of annotation errors. The performance of corrective approaches suffers most strongly from this, leading to a less profound difference to uncertainty-based annotations.
Keywords: glioblastoma; interactive; online; segmentation.