Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0–2 years

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2020-09, Vol.218, p.116946-116946, Article 116946
• Main Authors: Zöllei, Lilla, Iglesias, Juan Eugenio, Ou, Yangming, Grant, P. Ellen, Fischl, Bruce
• Format: Article
• Language: English
• Subjects: Age; Aging; Algorithms; Atlases as Topic; Automation; Babies; Brain surface; Cerebral cortex; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - growth & development; Computational neuroscience; Datasets; Developmental disabilities; Female; FreeSurfer; Humans; Image processing; Image Processing, Computer-Assisted - methods; Infant; Infant, Newborn; Infants; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mental disorders; Morphometry; MRI; Myelin Sheath; Myelination; Neurodevelopmental disorders; Neuroimaging; Neuroimaging - methods; Newborn babies; Pediatrics; Probability; Segmentation; Software
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116946

The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package. •FreeSurfer is a widely used and evolving processing suite for brain MRIs.•Morphometric brain analysis in infants has lagged behind that of adults.•Our novel pipeline accommodates T1-weighted brain MRIs from 0 to 2 year-olds.•Its unified approach is valid across a full age range, without foregoing accuracy.•Similar applications are largely derived from newborns only (often preterm).