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

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

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

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 publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages703-710
Number of pages8
Volume16
EditionPt 1
Publication statusPublished - 2013 Dec 1
Externally publishedYes

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ASJC Scopus subject areas

  • Medicine(all)

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 Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 16, pp. 703-710)