Automatic neonatal brain tissue segmentation with MRI

Vedran Srhoj-Egekher; Manon J. N. L. Benders; Max A. Viergever; Ivana Išgum

doi:10.1117/12.2006653

13 March 2013 Automatic neonatal brain tissue segmentation with MRI

Vedran Srhoj-Egekher, Manon J. N. L. Benders, Max A. Viergever, Ivana Išgum

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86691K (2013) https://doi.org/10.1117/12.2006653
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Volumetric measurements of neonatal brain tissue classes have been suggested as an indicator of long-term neurodevelopmental performance. To obtain these measurements, accurate brain tissue segmentation is needed. We propose a novel method for automatic segmentation of cortical grey matter (CoGM), unmyelinated white matter (UWM), myelinated white matter (MWM), basal ganglia and thalami, brainstem, cerebellum, ventricles, and cerebrospinal fluid in the extracerebral space (CSF) in MRI scans of the brain in preterm infants. For this project, seven preterm born infants, scanned at term equivalent age were used. Axial T1- and T2- weighted scans were acquired with 3T MRI scanner. The automatic segmentation was performed in three subsequent stages where each tissue was labeled. First, a multi-atlas-based segmentation (MAS) was employed to obtain localized, subject specific spatially varying priors for each tissue. Next, based on these priors, two-class classification with k-nearest neighbor (kNN) classifier was performed to obtain the segmentation of each tissue type separately. Last, to refine the final result, and to achieve the segmentation along the tissue boundaries, a multiclass naive Bayes classifier was employed. The results were evaluated against the manually set reference standard and quantified in terms of Dice coefficient (DC) and modified Hausdorff distance (MHD), defined as 95th-percentile of the Hausdorff distance. On average, the method achieved the following DCs: 0.87 for CoGM, 0.91 for UWM, 0.60 for MWM, 0.93 for basal ganglia and thalami, 0.87 for brainstem, 0.94 for cerebellum, 0.86 for ventricles, 0.82 for CSF. The obtained average MHDs were 0.48 mm, 0.44 mm, 3.09 mm, 0.39 mm, 0.62 mm, 0.35 mm, 1.75 mm, 1.13 mm, for each tissue, respectively. The proposed methods achieved high segmentation accuracy for all tissues, except for MWM, and it provides a tool for quantification of brain tissue volumes in axial MRI scans of preterm born infants.

Citation Download Citation

Vedran Srhoj-Egekher, Manon J. N. L. Benders, Max A. Viergever, and Ivana Išgum "Automatic neonatal brain tissue segmentation with MRI", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691K (13 March 2013); https://doi.org/10.1117/12.2006653

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available