Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age.

Authors

Ting Guo, Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada
Julie L Winterburn, Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Kimel Family Translational Imaging, Genetics Research Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Canada
Jon Pipitone, Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Kimel Family Translational Imaging, Genetics Research Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Canada
Emma G Duerden, Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada
Min Tae M Park, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health Research Institute, Verdun, QC, Canada
Vann Chau, Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada
Kenneth J Poskitt, Department of Pediatrics, University of British Columbia and Child and Family Research Institute, Vancouver, BC, Canada
Ruth E Grunau, Department of Pediatrics, University of British Columbia and Child and Family Research Institute, Vancouver, BC, Canada
Anne Synnes, Department of Pediatrics, University of British Columbia and Child and Family Research Institute, Vancouver, BC, Canada
Steven P Miller, Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada
M Mallar Chakravarty, Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health Research Institute, Verdun, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada

Document Type

Article

Publication Date

1-1-2015

Journal

Neuroimage Clin

Volume

9

First Page

176

Last Page

193

URL with Digital Object Identifier

10.1016/j.nicl.2015.07.019

Abstract

INTRODUCTION: The hippocampus, a medial temporal lobe structure central to learning and memory, is particularly vulnerable in preterm-born neonates. To date, segmentation of the hippocampus for preterm-born neonates has not yet been performed early-in-life (shortly after birth when clinically stable). The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the hippocampi of preterm neonates on their brain MRIs acquired at not only term-equivalent age but also early-in-life.

METHODS: First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm neonates and apply this protocol on 22 early-in-life and 22 term images. These manual segmentations are considered the gold standard in assessing the automatic segmentations. MAGeT-Brain, automatic hippocampal segmentation pipeline, requires only a small number of input atlases and reduces the registration and resampling errors by employing an intermediate template library. We assess the segmentation accuracy of MAGeT-Brain in three validation studies, evaluate the hippocampal growth from early-in-life to term-equivalent age, and study the effect of preterm birth on the hippocampal volume. The first experiment thoroughly validates MAGeT-Brain segmentation in three sets of 10-fold Monte Carlo cross-validation (MCCV) analyses with 187 different groups of input atlases and templates. The second experiment segments the neonatal hippocampi on 168 early-in-life and 154 term images and evaluates the hippocampal growth rate of 125 infants from early-in-life to term-equivalent age. The third experiment analyzes the effect of gestational age (GA) at birth on the average hippocampal volume at early-in-life and term-equivalent age using linear regression.

RESULTS: The final segmentations demonstrate that MAGeT-Brain consistently provides accurate segmentations in comparison to manually derived gold standards (mean Dice's Kappa > 0.79 and Euclidean distance

CONCLUSIONS: MAGeT-Brain is capable of segmenting hippocampi accurately in preterm neonates, even at early-in-life. Hippocampal asymmetry with a larger right side is demonstrated on early-in-life images, suggesting that this phenomenon has its onset in the 3rd trimester of gestation. Hippocampal volume assessed at the time of early-in-life and term-equivalent age is linearly associated with GA at birth, whereby smaller volumes are associated with earlier birth.