@inproceedings{478f617eff6c4baeb696f8fe23cd5638,
title = "Bayesian automated cortical segmentation for neonatal MRI",
abstract = "Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.",
keywords = "Brain tissue segmentation, Cortical grey matter (cGM), Magnetic resonance imaging (MRI), Neonatal brain, Prematurity, Unmyelinated white matter (uWM)",
author = "Zane Chou and Natacha Paquette and Bhavana Ganesh and Yalin Wang and Rafael Ceschin and Nelson, {Marvin D.} and Luke Macyszyn and Bilwaj Gaonkar and Ashok Panigrahy and Natasha Lepore",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017 ; Conference date: 05-10-2017 Through 07-10-2017",
year = "2017",
doi = "10.1117/12.2285217",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Natasha Lepore and Jorge Brieva and Garcia, {Juan David} and Eduardo Romero",
booktitle = "13th International Conference on Medical Information Processing and Analysis",
}