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

Publication ,  Journal Article
Wang, L; Shi, F; Li, G; Lin, W; Gilmore, JH; Shen, D
Published in: Med Image Comput Comput Assist Interv
2013

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.

Duke Scholars

Published In

Med Image Comput Comput Assist Interv

DOI

Publication Date

2013

Volume

16

Issue

Pt 1

Start / End Page

703 / 710

Location

Germany

Related Subject Headings

  • Systems Integration
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Multimodal Imaging
  • Male
  • Magnetic Resonance Imaging
  • Infant, Newborn
  • Infant
  • Image Interpretation, Computer-Assisted
 

Citation

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Chicago
ICMJE
MLA
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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. Med Image Comput Comput Assist Interv, 16(Pt 1), 703–710. https://doi.org/10.1007/978-3-642-40811-3_88
Wang, Li, Feng Shi, Gang Li, Weili Lin, John H. Gilmore, and Dinggang Shen. “Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation.Med Image Comput Comput Assist Interv 16, no. Pt 1 (2013): 703–10. 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. Med Image Comput Comput Assist Interv. 2013;16(Pt 1):703–10.
Wang, Li, et al. “Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation.Med Image Comput Comput Assist Interv, vol. 16, no. Pt 1, 2013, pp. 703–10. Pubmed, doi: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. Med Image Comput Comput Assist Interv. 2013;16(Pt 1):703–710.

Published In

Med Image Comput Comput Assist Interv

DOI

Publication Date

2013

Volume

16

Issue

Pt 1

Start / End Page

703 / 710

Location

Germany

Related Subject Headings

  • Systems Integration
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Multimodal Imaging
  • Male
  • Magnetic Resonance Imaging
  • Infant, Newborn
  • Infant
  • Image Interpretation, Computer-Assisted