Non-local atlas-guided multi-channel forest learning for human brain labeling

Guangkai Ma, Yaozong Gao, Guorong Wu, Ligang Wu, Dinggang Shen

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

1 Citation (Scopus)


Labeling MR brain images into anatomically meaningful regions is important in many quantitative brain researches. In many existing label fusion methods, appearance information is widely used. Meanwhile, recent progress in computer vision suggests that the context feature is very useful in identifying an object from a complex scene. In light of this, we propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). In particular, we employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and the target labels (i.e., corresponding to certain anatomical structures). Moreover, to accommodate the high inter-subject variations, we further extend our learning-based label fusion to a multi-atlas scenario, i.e., we train a random forest for each atlas and then obtain the final labeling result according to the consensus of all atlases. We have comprehensively evaluated our method on both LONI-LBPA40 and IXI datasets, and achieved the highest labeling accuracy, compared to the state-of-the-art methods in the literature.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings
EditorsAlejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319245737
Publication statusPublished - 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 2015 Oct 52015 Oct 9

Publication series

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


Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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