Sparse patch-based label fusion for multi-atlas segmentation

Daoqiang Zhang, Qimiao Guo, Guorong Wu, Dinggang Shen

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

42 Citations (Scopus)

Abstract

Patch-based label fusion methods have shown great potential in multi-atlas segmentation. It is crucial for patch-based labeling methods to determine appropriate graphs and corresponding weights to better link patches in the input image with those in atlas images. Currently, two independent steps are performed, i.e., first constructing graphs based on the fixed image neighborhood and then computing weights based on the heat kernel for all patches in the neighborhood. In this paper, we first show that many existing label fusion methods can be unified into a graph-based framework, and then propose a novel method for simultaneously deriving both graph adjacency structure and graph weights based on the sparse representation, to perform multi-atlas segmentation. Our motivation is that each patch in the input image can be reconstructed by the sparse linear superposition of patches in the atlas images, and the reconstruction coefficients can be used to deduce both graph structure and weights simultaneously. Experimental results on segmenting brain anatomical structures from magnetic resonance images (MRI) show that our proposed method achieves significant improvements over previous patch-based methods, as well as other conventional label fusion methods.

Original languageEnglish
Title of host publicationMultimodal Brain Image Analysis - Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Proceedings
Pages94-102
Number of pages9
DOIs
Publication statusPublished - 2012
Event2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 2012 Oct 12012 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7509 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period12/10/112/10/5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhang, D., Guo, Q., Wu, G., & Shen, D. (2012). Sparse patch-based label fusion for multi-atlas segmentation. In Multimodal Brain Image Analysis - Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Proceedings (pp. 94-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7509 LNCS). https://doi.org/10.1007/978-3-642-33530-3_8