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.
ACCESS THE FULL ARTICLE
Jens Petersen, Martin Bendszus, Jürgen Debus, Sabine Heiland, Klaus H. Maier-Hein, "Effective user guidance in online interactive semantic segmentation," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341V (3 March 2017);