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Lifted Auto-Context Forests for Brain Tumour Segmentation

  • Loic Le FolgocEmail author
  • Aditya V. Nori
  • Siddharth Ancha
  • Antonio Criminisi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

Abstract

We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on hold-out estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.

Notes

Acknowledgment

The authors would like to thank the Microsoft–Inria Joint Centre for partially funding this work.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Loic Le Folgoc
    • 1
    Email author
  • Aditya V. Nori
    • 1
  • Siddharth Ancha
    • 1
  • Antonio Criminisi
    • 1
  1. 1.Microsoft Research CambridgeCambridgeUK

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