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Laplacian Forests: Semantic Image Segmentation by Guided Bagging

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.
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Contributor : Herve Lombaert Connect in order to contact the contributor
Submitted on : Tuesday, November 4, 2014 - 10:48:26 AM
Last modification on : Friday, November 18, 2022 - 9:27:33 AM
Long-term archiving on: : Thursday, February 5, 2015 - 10:25:59 AM


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Herve Lombaert, Darko Zikic, Antonio Criminisi, Nicholas Ayache. Laplacian Forests: Semantic Image Segmentation by Guided Bagging. MICCAI 2014 - 17th International Conference Medical Image Computing and Computer-Assisted Intervention, Sep 2014, Boston, United States. ⟨10.1007/978-3-319-10470-6_62⟩. ⟨hal-01009672⟩



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