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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication13th International Conference on Medical Information Processing and Analysis
EditorsNatasha Lepore, Jorge Brieva, Juan David Garcia, Eduardo Romero
PublisherSPIE
ISBN (Electronic)9781510616332
DOIs
StatePublished - 2017
Event13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017 - San Andres Island, Colombia
Duration: Oct 5 2017Oct 7 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10572
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017
CountryColombia
CitySan Andres Island
Period10/5/1710/7/17

Keywords

  • Brain tissue segmentation
  • Cortical grey matter (cGM)
  • Magnetic resonance imaging (MRI)
  • Neonatal brain
  • Prematurity
  • Unmyelinated white matter (uWM)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Chou, Z., Paquette, N., Ganesh, B., Wang, Y., Ceschin, R., Nelson, M. D., Macyszyn, L., Gaonkar, B., Panigrahy, A., & Lepore, N. (2017). Bayesian automated cortical segmentation for neonatal MRI. In N. Lepore, J. Brieva, J. D. Garcia, & E. Romero (Eds.), 13th International Conference on Medical Information Processing and Analysis [105720R] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10572). SPIE. https://doi.org/10.1117/12.2285217