Using simulated user interactions in a MRI glioblastoma segmentation task, we show that if the user possesses knowledge of the correct segmentation it is significantly (p ≤ 0.009) better to present data and current segmentation to the user in such a manner that they can easily identify falsely classified regions compared to guiding the user to regions where the classifier exhibits high uncertainty, resulting in differences of mean Dice scores between +0.070 (Whole tumor) and +0.136 (Tumor Core) after 20 iterations. The annotation process should cover all classes equally, which results in a significant (p ≤ 0.002) improvement compared to completely random annotations anywhere in falsely classified regions for small tumor regions such as the necrotic tumor core (mean Dice +0.151 after 20 it.) and non-enhancing abnormalities (mean Dice +0.069 after 20 it.). These findings provide important insights for the development of efficient interactive segmentation systems and user interfaces.
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