title: Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation.
abstract: Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6-8months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter.
authors.affiliation: IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
authors.affiliation: IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; Department of Computer Science, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.affiliation: Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: John H
authors.initials: JH
authors.lastname: Gilmore
authors.affiliation: MRI Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
authors.affiliation: IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea. Electronic address: dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D008297:Male; D009413:Nerve Fibers, Myelinated
keywords: Anatomical constraint; Infant brain images; Isointense stage; Low contrast; Sparse representation; Tissue segmentation