Automatic atlas-based segmentation of brain white matter in neonates at risk for neurodevelopmental disorders

Lúcia Fonseca, C. van Pul, N. Lori, R. van den Boom, P. Andriessen, J. Buijs, Anna Vilanova Bartroli

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

1 Citation (Scopus)

Abstract

Very preterm infants, < 32 weeks gestation, are at high risk for brain injury. Cognitive deficits are often diagnosed at a later stage, since there are no available predictive biomarkers in the neonatal period. The maturation of specific white matter (WM) brain structures is considered a promising early-stage biomarker. With Diffusion Tensor Imaging (DTI) tractography, an in vivo and non-invasive evaluation of these anatomical structures is possible. We developed an automatic tractography segmentation pipeline, which allows for maturation assessment of the different segmented WM structures. Our segmentation pipeline is atlas-based, specifically designed for premature neonates at term equivalent age. In order to better make use of global information from tractography, all processing is done in the fiber domain. Segmented fiber bundles are further automatically quantified with respect to volume and anisotropy. Of the 24 automatically segmented neonatal tractographies, only three contained more than 30% mislabeled fibers. Results show no dependency to WM pathology. By automatically segmenting WM, we reduced the user-dependency and bias characteristic of manual methods. This study assesses the structure of the neonatal brain based on an automatic WM segmentation in the fiber domain method using DTI tractography data.

Original languageEnglish
Title of host publicationModeling, Analysis, and Visualization of Anisotropy
EditorsT. Schultz, E. Özarslan, I. Hotz
Place of PublicationCham
PublisherSpringer
Pages355-372
Number of pages18
ISBN (Electronic)978-3-319-61358-1
ISBN (Print)978-3-319-61357-4
DOIs
Publication statusPublished - 2017

Publication series

NameMathematics and Visualization
PublisherSpringer
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X

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