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TY - CONF AU - Top, Andrew AU - Hamarneh, Ghassan AU - Abugharbieh, Rafeef ED - Fichtinger, Gabor ED - Martel, Anne ED - Peters, Terry PY - 2011 DA - 2011// TI - Active Learning for Interactive 3D Image Segmentation BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011 SP - 603 EP - 610 PB - Springer Berlin Heidelberg CY - Berlin, Heidelberg AB - We propose a novel method for applying active learning strategies to interactive 3D image segmentation. Active learning has been recently introduced to the field of image segmentation. However, so far discussions have focused on 2D images only. Here, we frame interactive 3D image segmentation as a classification problem and incorporate active learning in order to alleviate the user from choosing where to provide interactive input. Specifically, we evaluate a given segmentation by constructing an “uncertainty field” over the image domain based on boundary, regional, smoothness and entropy terms. We then calculate and highlight the plane of maximal uncertainty in a batch query step. The user can proceed to guide the labeling of the data on the query plane, hence actively providing additional training data where the classifier has the least confidence. We validate our method against random plane selection showing an average DSC improvement of 10% in the first five plane suggestions (batch queries). Furthermore, our user study shows that our method saves the user 64% of their time, on average. SN - 978-3-642-23626-6 ID - 10.1007/978-3-642-23626-6_74 ER -