Hierarchical label fusion with multiscale feature representation and label-specific patch partition

Guorong Wu, Dinggang Shen

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

4 Citations (Scopus)

Abstract

Recently, patch-based label fusion methods have achieved many successes in medical imaging area. After registering atlas images to the target image, the label at each target image point can be subsequently determined by checking the patchwise similarities between the underlying target image patch and all atlas image patches. Apparently, the definition of patchwise similarity is critical in label fusion. However, current methods often simply use entire image patch with fixed patch size throughout the entire label fusion procedure, which could be insufficient to distinguish complex shape/appearance patterns of anatomical structures in medical imaging scenario. In this paper, we address the above limitations at three folds. First, we assign each image patch with multiscale feature representations such that both local and semi-local image information can be encoded to increase robustness of measuring patchwise similarity in label fusion. Second, since multiple variable neighboring structures could present in one image patch, simply computing patchwise similarity based on the entire image patch is not specific to the particular structure of interest under labeling and can be easily misled by the surrounding variable structures in the same image patch. Thus, we partition each atlas patch into a set of new label-specific atlas patches according to the existing label information in the atlas images. Then, the new label-specific atlas patches can be more specific and flexible for label fusion than using the entire image patch, since the complex image patch has now been semantically divided into several distinct patterns. Finally, in order to correct the possible mis-labeling, we hierarchically improve the label fusion result in a coarse-to-fine manner by iteratively repeating the label fusion procedure with the gradually-reduced patch size. More accurate label fusion results have been achieved by our hierarchical label fusion method with multiscale feature presentations upon label-specific atlas patches.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages299-306
Number of pages8
Volume8673 LNCS
EditionPART 1
ISBN (Print)9783319104034
DOIs
Publication statusPublished - 2014 Jan 1
Externally publishedYes
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: 2014 Sep 142014 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8673 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston, MA
Period14/9/1414/9/18

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Wu, G., & Shen, D. (2014). Hierarchical label fusion with multiscale feature representation and label-specific patch partition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 8673 LNCS, pp. 299-306). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8673 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-319-10404-1_38