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Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation
Li Wang1, Feng Shi1, Gang Li1, Weili Lin1, John H. Gilmore2, and Dinggang Shen1
1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
2Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA
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.
LNCS 8149, p. 703 ff.
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© Springer-Verlag Berlin Heidelberg 2013