Bayesian automated cortical segmentation for neonatal MRI

Zane Chou; Natacha Paquette; Bhavana Ganesh; Yalin Wang; Rafael Ceschin; Marvin D. Nelson; Luke Macyszyn; Bilwaj Gaonkar; Ashok Panigrahy; Natasha Lepore

doi:10.1117/12.2285217

17 November 2017 Bayesian automated cortical segmentation for neonatal MRI

Zane Chou, Natacha Paquette, Bhavana Ganesh, Yalin Wang, Rafael Ceschin, Marvin D. Nelson, Luke Macyszyn, Bilwaj Gaonkar, Ashok Panigrahy, Natasha Lepore

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Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 105720R (2017) https://doi.org/10.1117/12.2285217
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

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.

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Zane Chou, Natacha Paquette, Bhavana Ganesh, Yalin Wang, Rafael Ceschin, Marvin D. Nelson, Luke Macyszyn, Bilwaj Gaonkar, Ashok Panigrahy, and Natasha Lepore "Bayesian automated cortical segmentation for neonatal MRI", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 105720R (17 November 2017); https://doi.org/10.1117/12.2285217

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KEYWORDS

Image segmentation

Brain

Magnetic resonance imaging

Neuroimaging

Binary data

Tissues

Photovoltaics

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