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Laplacian Forests: Semantic Image Segmentation by Guided Bagging

  • Herve Lombaert
  • Darko Zikic
  • Antonio Criminisi
  • Nicholas Ayache
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

This paper presents a new, efficient and accurate technique for the semantic segmentation of medical images. The paper builds upon the successful random decision forests model and improves on it by modifying the way in which randomness is injected into the tree training process. The contribution of this paper is two-fold. First, we replace the conventional bagging procedure (the uniform sampling of training images) with a guided bagging approach, which exploits the inherent structure and organization of the training image set. This allows the creation of decision trees that are specialized to a specific sub-type of images in the training set. Second, the segmentation of a previously unseen image happens via selection and application of only the trees that are relevant to the given test image. Tree selection is done automatically, via the learned image embedding, with more precisely a Laplacian eigenmap. We, therefore, call the proposed approach Laplacian Forests. We validate Laplacian Forests on a dataset of 256, manually segmented 3D CT scans of patients showing high variability in scanning protocols, resolution, body shape and anomalies. Compared with conventional decision forests, Laplacian Forests yield both higher training efficiency, due to the local analysis of the training image space, as well as higher segmentation accuracy, due to the specialization of the forest to image sub-types.

Keywords

Test Image Training Image Image Space Segmentation Accuracy Tree Weighting 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Herve Lombaert
    • 1
    • 2
  • Darko Zikic
    • 2
  • Antonio Criminisi
    • 2
  • Nicholas Ayache
    • 1
  1. 1.Asclepios TeamINRIA Sophia-AntipolisFrance
  2. 2.Microsoft ResearchCambridgeUK

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