Active Learning for Interactive 3D Image Segmentation

  • Andrew Top
  • Ghassan Hamarneh
  • Rafeef Abugharbieh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)


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.


Image Segmentation Hyperspectral Image Query Plane Query Suggestion Active Learning Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrew Top
    • 1
  • Ghassan Hamarneh
    • 1
  • Rafeef Abugharbieh
    • 2
  1. 1.Medical Image Analysis LabSimon Fraser UniversityCanada
  2. 2.Biomedical Signal and Image Computing LabUniversity of British ColumbiaCanada

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