Spatially adaptive random forests

Ezequiel Geremia, Bjoern H. Menze, Nicholas Ayache

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

43 Scopus citations

Abstract

Medical imaging protocols produce large amounts of multi-modal volumetric images. The large size of the datasets contributes to the success of supervised discriminative methods for semantic image segmentation. Classifying relevant structures in medical images is challenging due to (a) the large size of data volumes, and (b) the severe class overlap in the feature space. Subsampling the training data addresses the first issue at the cost of discarding potentially useful image information. Increasing feature dimensionality addresses the second but requires dense sampling. We propose a general and efficient solution to these problems. 'Spatially Adaptive Random Forests' (SARF) is a supervised learning algorithm. SARF aims at automatic semantic labelling of large medical volumes. During training, it learns the optimal image sampling associated to the classification task. During testing, the algorithm quickly handles the background and focuses challenging image regions to refine the classification. SARF demonstrated top performance in the context of multi-class gliomas segmentation in multi-modal MR images.

Original languageEnglish
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages1344-1347
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: 7 Apr 201311 Apr 2013

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period7/04/1311/04/13

Keywords

  • hierarchical
  • multi-scale
  • random forest
  • sampling
  • segmentation
  • structured labelling

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