Minimizing joint risk of mislabeling for iterative patch-based label fusion

Guorong Wu, Qian Wang, Shu Liao, Daoqiang Zhang, Feiping Nie, Dinggang Shen

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

Abstract

Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling in the non-local manner has been widely investigated to alleviate the possible misalignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases in conventional methods are generally computed independently and thus lack the capability of preventing the ambiguous atlas patches from contributing to the label fusion. More critically, these weights are often calculated based only on the simple patch similarity, thus not necessarily providing optimal solution for label fusion. To address these issues, we present a novel patch-based label fusion method in multi-atlas scenario, for the goal of labeling each voxel in the target image by the best representative atlas patches that also have the lowest joint risk of mislabeling. Specifically, sparse coding is used to select a small number of atlas patches which best represent the underlying patch at each point of the target image, thus minimizing the chance of including the misleading atlas patches for labeling. Furthermore, we examine the joint risk of any pair of atlas patches in making similar labeling error, by analyzing the correlation of their morphological error patterns and also the labeling consensus among atlases. This joint risk will be further recursively updated based on the latest labeling results to correct the possible labeling errors. To demonstrate the performance of our proposed method, we have evaluated it on both whole brain parcellation and hippocampus segmentation, and achieved promising labeling results, compared with the state-of- the-art methods.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages551-558
Number of pages8
Volume8151 LNCS
EditionPART 3
DOIs
Publication statusPublished - 2013 Oct 24
Externally publishedYes
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 2013 Sep 222013 Sep 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period13/9/2213/9/26

Fingerprint

Atlas
Labeling
Patch
Labels
Fusion
Fusion reactions
Target
Sparse Coding
Hippocampus
Neuroscience
Misalignment
Voxel
Medical Image
Ambiguous
Lowest
Brain
Segmentation
Optimal Solution
Scenarios

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wu, G., Wang, Q., Liao, S., Zhang, D., Nie, F., & Shen, D. (2013). Minimizing joint risk of mislabeling for iterative patch-based label fusion. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 8151 LNCS, pp. 551-558). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-40760-4_69

Minimizing joint risk of mislabeling for iterative patch-based label fusion. / Wu, Guorong; Wang, Qian; Liao, Shu; Zhang, Daoqiang; Nie, Feiping; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8151 LNCS PART 3. ed. 2013. p. 551-558 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3).

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

Wu, G, Wang, Q, Liao, S, Zhang, D, Nie, F & Shen, D 2013, Minimizing joint risk of mislabeling for iterative patch-based label fusion. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 8151 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8151 LNCS, pp. 551-558, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 13/9/22. https://doi.org/10.1007/978-3-642-40760-4_69
Wu G, Wang Q, Liao S, Zhang D, Nie F, Shen D. Minimizing joint risk of mislabeling for iterative patch-based label fusion. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 8151 LNCS. 2013. p. 551-558. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-40760-4_69
Wu, Guorong ; Wang, Qian ; Liao, Shu ; Zhang, Daoqiang ; Nie, Feiping ; Shen, Dinggang. / Minimizing joint risk of mislabeling for iterative patch-based label fusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8151 LNCS PART 3. ed. 2013. pp. 551-558 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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