title: LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images.
abstract: Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy.
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; 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: 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.
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, Republic of Korea. Electronic address: dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007223:Infant; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods
keywords: Context feature; Infant brain images; Isointense stage; Multi-modality; Random forest; Tissue segmentation