Entangled decision forests and their application for semantic segmentation of CT images

Albert Montillo, Jamie Shotton, John Winn, Juan Eugenio Iglesias, Dimitri Metaxas, Antonio Criminisi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

101 Scopus citations

Abstract

This work addresses the challenging problem of simultaneously segmenting multiple anatomical structures in highly varied CT scans. We propose the entangled decision forest (EDF) as a new discriminative classifier which augments the state of the art decision forest, resulting in higher prediction accuracy and shortened decision time. Our main contribution is two-fold. First, we propose entangling the binary tests applied at each tree node in the forest, such that the test result can depend on the result of tests applied earlier in the same tree and at image points offset from the voxel to be classified. This is demonstrated to improve accuracy and capture long-range semantic context. Second, during training, we propose injecting randomness in a guided way, in which node feature types and parameters are randomly drawn from a learned (non-uniform) distribution. This further improves classification accuracy. We assess our probabilistic anatomy segmentation technique using a labeled database of CT image volumes of 250 different patients from various scan protocols and scanner vendors. In each volume, 12 anatomical structures have been manually segmented. The database comprises highly varied body shapes and sizes, a wide array of pathologies, scan resolutions, and diverse contrast agents. Quantitative comparisons with state of the art algorithms demonstrate both superior test accuracy and computational efficiency.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 22nd International Conference, IPMI 2011, Proceedings
Pages184-196
Number of pages13
DOIs
StatePublished - Jun 30 2011
Event22nd International Conference on Information Processing in Medical Imaging, IPMI 2011 - Kloster Irsee, Germany
Duration: Jul 3 2011Jul 8 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6801 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Information Processing in Medical Imaging, IPMI 2011
CountryGermany
CityKloster Irsee
Period7/3/117/8/11

Keywords

  • CT
  • Entanglement
  • auto-context
  • decision forests
  • segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Montillo, A., Shotton, J., Winn, J., Iglesias, J. E., Metaxas, D., & Criminisi, A. (2011). Entangled decision forests and their application for semantic segmentation of CT images. In Information Processing in Medical Imaging - 22nd International Conference, IPMI 2011, Proceedings (pp. 184-196). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6801 LNCS). https://doi.org/10.1007/978-3-642-22092-0_16