NDLI: Multi-Atlas Multi-Shape Segmentation of Fetal Brain MRI for Volumetric and Morphometric Analysis of Ventriculomegaly

Content Provider	PubMed Central
Author	Ali, Gholipour Akhondi-asl, Alireza Estroff, Judy A. Warfield, Simon K.
Copyright Year	2012
Abstract	The recent development of motion robust super-resolution fetal brain MRI holds out the potential for dramatic new advances in volumetric and morphometric analysis. Volumetric analysis based on volumetric and morphometric biomarkers of the developing fetal brain must include segmentation. Automatic segmentation of fetal brain MRI is challenging, however, due to the highly variable size and shape of the developing brain; possible structural abnormalities; and the relatively poor resolution of fetal MRI scans. To overcome these limitations, we present a novel, constrained, multi-atlas, multi-shape automatic segmentation method that specifically addresses the challenge of segmenting multiple structures with similar intensity values in subjects with strong anatomic variability. Accordingly, we have applied this method to shape segmentation of normal, dilated, or fused lateral ventricles for quantitative analysis of ventriculomegaly (VM), which is a pivotal finding in the earliest stages of fetal brain development, and warrants further investigation. Utilizing these innovative techniques, we introduce novel volumetric and morphometric biomarkers of VM comparing these values to those that are generated by standard methods of VM analysis, i.e., by measuring the ventricular atrial diameter (AD) on manually selected sections of 2D ultrasound or 2D MRI. To this end, we studied 25 normal and abnormal fetuses in the gestation age (GA) range of 19 to 39 weeks (mean=28.26, stdev=6.56). This heterogenous dataset was essentially used to 1) validate our segmentation method for normal and abnormal ventricles; and 2) show that the proposed biomarkers may provide improved detection of VM as compared to the AD measurement.
Related Links	http://dx.doi.org/10.1016/j.neuroimage.2012.01.128
Ending Page	1831
Page Count	13
Starting Page	1819
File Format	PDF
ISSN	10538119
e-ISSN	10959572
Journal	Neuroimage
Issue Number	3
Volume Number	60
Language	English
Publisher Date	2012-04-01
Access Restriction	Open
Subject Keyword	Cognitive Neuroscience Neurology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Neurology Cognitive Neuroscience

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