Automatic Parcellation of Longitudinal Cortical Surfaces

Preterm birth incidence is a main cause of developing cognitive and neurologic disorders in childhood especially with children who are born extremely preterm. The human brain experiences significant functional and morphological changes at early development before and around birth. Understanding and modeling brain normal growth and cortical changes in early development are the keys to understanding and tracking neurologic disorders. The objective of this dissertation is to develop methods for longitudinally modeling brain development in order to provide researchers with tools for understanding normal growth patterns and for designing interventions that minimize potential preterm brain injury. We present a novel algorithm for longitudinally parcellating the developing brain at different stages of development. The algorithm assigns each cortical location to a neuroanatomical brain structure during early development. A labeled newborn brain atlas at 41 weeks gestational age (GA) is used to propagate labels of anatomical regions of interest to a spatio-temporal atlas, which provides a dynamic model of brain development at each week between 28-44 GA weeks. First, cortical labels from the volume of the newborn brain are propagated to an age-matched cortical surface from the spatio-temporal atlas. Then, labels are propagated across the cortical surfaces of each week of the spatio-temporal atlas by registering successive cortical surfaces using a new approach and using an energy optimization function. This procedure incorporates local and global, spatial and temporal information when assigning the labels. The result is a complete parcellation of 17 neonatal brain surfaces with similar points per labels distributions across weeks.

Author

• Alassaf, Manal Hussein

Language

• en

Keyword

• Spatio-temporal Parcellation
• Developing Brain Parcellation
• Developing Brain
• Longitudinal Parcellation
• Developing Brain Segmentation
• Neonatal brain Atlas

Date created

• 2015

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Computer Science

Degree

• Ph.D.

Advisor

• Hahn, James K

Committee Member(s)

• Koubeissi, Mohamad Z
• Youssef, Abdou S
• Monteleoni, Claire

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/ks65hc27r