title: Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation.
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.
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Li
authors: L
authors: Wang
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Feng
authors: F
authors: Shi
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Gang
authors: G
authors: Li
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Weili
authors: W
authors: Lin
authors: Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
authors: John H
authors: JH
authors: Gilmore
authors: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors: Dinggang
authors: D
authors: Shen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007223:Infant; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018511:Systems Integration