title: Patch-based augmentation of Expectation-Maximization for brain MRI tissue segmentation at arbitrary age after premature birth.
abstract: Accurate automated tissue segmentation of premature neonatal magnetic resonance images is a crucial task for quantification of brain injury and its impact on early postnatal growth and later cognitive development. In such studies it is common for scans to be acquired shortly after birth or later during the hospital stay and therefore occur at arbitrary gestational ages during a period of rapid developmental change. It is important to be able to segment any of these scans with comparable accuracy. Previous work on brain tissue segmentation in premature neonates has focused on segmentation at specific ages. Here we look at solving the more general problem using adaptations of age specific atlas based methods and evaluate this using a unique manually traced database of high resolution images spanning 20 gestational weeks of development. We examine the complimentary strengths of age specific atlas-based Expectation-Maximization approaches and patch-based methods for this problem and explore the development of two new hybrid techniques, patch-based augmentation of Expectation-Maximization with weighted fusion and a spatial variability constrained patch search. The former approach seeks to combine the advantages of both atlas- and patch-based methods by learning from the performance of the two techniques across the brain anatomy at different developmental ages, while the latter technique aims to use anatomical variability maps learnt from atlas training data to locally constrain the patch-based search range. The proposed approaches were evaluated using leave-one-out cross-validation. Compared with the conventional age specific atlas-based segmentation and direct patch based segmentation, both new approaches demonstrate improved accuracy in the automated labeling of cortical gray matter, white matter, ventricles and sulcal cortical-spinal fluid regions, while maintaining comparable results in deep gray matter.
authors.affiliation: Biomedical Image Computing Group, Department of Pediatrics, Bioengineering and Radiology, University of Washington, HSB, NE Pacific St., Seattle, WA 98195, USA. Electronic address: liumyzoe@uw.edu.
authors.forename: Mengyuan
authors.initials: M
authors.lastname: Liu
authors.affiliation: Biomedical Image Computing Group, Department of Pediatrics, Bioengineering and Radiology, University of Washington, HSB, NE Pacific St., Seattle, WA 98195, USA.
authors.forename: Averi
authors.initials: A
authors.lastname: Kitsch
authors.affiliation: Center for Brain and Mental Health, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Department of Pediatrics, University of Toronto, Toronto, ON M5S, Canada.
authors.forename: Steven
authors.initials: S
authors.lastname: Miller
authors.affiliation: Center for Brain and Mental Health, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Department of Pediatrics, University of Toronto, Toronto, ON M5S, Canada.
authors.forename: Vann
authors.initials: V
authors.lastname: Chau
authors.affiliation: Department of Pediatrics, University of British Columbia, Vancouver, BC V5Z 4H4, Canada.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Poskitt
authors.affiliation: Institut Mines Télécom, Télécom Bretagne, Latim INSERM U1101, Brest, France.
authors.forename: Francois
authors.initials: F
authors.lastname: Rousseau
authors.affiliation: Department of Radiology, Seattle Children's Hospital, Seattle, WA 98105, USA.
authors.forename: Dennis
authors.initials: D
authors.lastname: Shaw
authors.affiliation: Biomedical Image Computing Group, Department of Pediatrics, Bioengineering and Radiology, University of Washington, HSB, NE Pacific St., Seattle, WA 98195, USA.
authors.forename: Colin
authors.initials: C
authors.lastname: Studholme
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007231:Infant, Newborn; D007234:Infant, Premature; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D059906:Neuroimaging / Q000379:methods*
keywords: Atlas-based; Expectation–Maximization; MRI; Patch-based; Premature neonates; Segmentation; Spatio-temporal