Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation

Li Wang, Feng Shi, Gang Li, Weili Lin, John H. Gilmore, Dinggang Shen

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

Abstract

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6-8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other 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)
Pages703-710
Number of pages8
Volume8149 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2013 Oct 23
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 1
Volume8149 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

Multimodality
Brain
Segmentation
Tissue
Sparse Representation
Image quality
Testing
Cross-validation
Image Quality
Patch
Inversion
Partial

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wang, L., Shi, F., Li, G., Lin, W., Gilmore, J. H., & Shen, D. (2013). Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 8149 LNCS, pp. 703-710). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8149 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-40811-3_88

Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation. / Wang, Li; Shi, Feng; Li, Gang; Lin, Weili; Gilmore, John H.; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8149 LNCS PART 1. ed. 2013. p. 703-710 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8149 LNCS, No. PART 1).

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

Wang, L, Shi, F, Li, G, Lin, W, Gilmore, JH & Shen, D 2013, Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 8149 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8149 LNCS, pp. 703-710, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 13/9/22. https://doi.org/10.1007/978-3-642-40811-3_88
Wang L, Shi F, Li G, Lin W, Gilmore JH, Shen D. Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 8149 LNCS. 2013. p. 703-710. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-40811-3_88
Wang, Li ; Shi, Feng ; Li, Gang ; Lin, Weili ; Gilmore, John H. ; Shen, Dinggang. / Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8149 LNCS PART 1. ed. 2013. pp. 703-710 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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