A dynamic 4D probabilistic atlas of the developing brain

Kuklisova‐Murgasova, Maria; Aljabar, Paul; Srinivasan, Latha; Counsell, Serena J.; D’Oria, Valentina; Serag, Ahmed; Gousias, Ioannis S.; Boardman, James P.; Rutherford, Mary A.; Edwards, A. David; Hajnal, Joseph V.; Rueckert, Daniel

doi:10.1016/j.neuroimage.2010.10.019

Cited by 252 publications

(229 citation statements)

References 61 publications

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“…Based on the mean transformation, we determine a new template space, transform all the subject images to the new template space and compute the average image in the new space. The template image built in this way can be regarded as an image which represents the average heart size, position and orientation and therefore avoids or reduces the bias towards a specific subject (Frangi et al, 2002;Kuklisova-Murgasova et al, 2011;Rueckert et al, 2001). Figure 3 illustrates the computation of the new template space based on the mean transformation.…”

Section: Template Imagementioning

confidence: 99%

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Bai

Shi

Marvao

et al. 2015

Medical Image Analysis

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116

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Section: Template Imagementioning

confidence: 99%

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Bai

Shi

Marvao

et al. 2015

Medical Image Analysis

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123

116

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“…Brain masks of the intracranial volume of the 40‐week EGA images were resampled from a publicly available neonate brain atlas described in (Kuklisova‐Murgasova et al. 2011) after a nonrigid registration between each image and the atlas template. Brain masks for the early 30‐week EGA images were resampled from their corresponding 40‐week EGA mask after affine alignment.…”

Section: Methodsmentioning

confidence: 99%

“…Anatomical priors were resampled in the same way as the brain masks from the same publicly available neonate brain atlas (Kuklisova‐Murgasova et al. 2011) after affine alignment with brain mask. An example of the brain segmentation for subject c is shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching

et al. 2016

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IntroductionInfants born extremely preterm (<28 weeks of gestation) are at risk of significant neurodevelopmental sequelae. In these infants birth coincides with a period of rapid brain growth and development, when the brain is also vulnerable to a range of insults. Mapping these changes is crucial for identifying potential biomarkers to predict early impairment.MethodsIn this study we use surface‐based spectral matching techniques to find an intrasubject longitudinal surface correspondence between the white–grey matter boundary at 30 and 40 weeks equivalent gestational age in nine extremely preterm born infants.ResultsUsing the resulting surface correspondence, we identified regions that undergo more cortical folding of the white–grey matter boundary during the preterm period by looking at changes in well‐known curvature measures. We performed Hotelling T2 statistics to evaluate the significance of our findings.DiscussionThe prefrontal and temporal lobes exhibit most development during the preterm period, especially in the left hemisphere. Such correspondences are a promising result as longitudinal measurements of change in cortical folding could provide insightful information about the mechanical properties of the underlying tissue and may be useful in inferring changes during growth and development in this vulnerable period.

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“…Assuming skull stripped images, we model the problem with K = 6 classes, each one with a corresponding digital atlas prior probability for white matter (WM), cortical grey matter (cGM), deep grey matter (dGM), cerebrospinal fluid (CSF), pons (Pon) and cerebellum (Cer) respectively at every voxel position. The anatomical priors provided by [14] are used here. The time point in the 4D atlas is chosen depending on the gestational age of the neonate under analysis.…”

Section: Map Expectation-maximization Segmentationmentioning

confidence: 99%

Adaptive Neonate Brain Segmentation

Cardoso

Melbourne

Kendall³

et al. 2011

Lecture Notes in Computer Science

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Abstract. Babies born prematurely are at increased risk of adverse neurodevelopmental outcomes. Recent advances suggest that measurement of brain volumes can help in defining biomarkers for neurodevelopmental outcome. These techniques rely on an accurate segmentation of the MRI data. However, due to lack of contrast, partial volume (PV) effect, the existence of both hypo-and hyper-intensities and significant natural and pathological anatomical variability, the segmentation of neonatal brain MRI is challenging. We propose a pipeline for image segmentation that uses a novel multi-model Maximum a posteriori Expectation Maximisation (MAP-EM) segmentation algorithm with a prior over both intensities and the tissue proportions, a B0 inhomogeneity correction, and a spatial homogeneity term through the use of a Markov Random Field. This robust and adaptive technique enables the segmentation of images with high anatomical disparity from a normal population. Furthermore, the proposed method implicitly models Partial Volume, mitigating the problem of neonatal white/grey matter intensity inversion. Experiments performed on a clinical cohort show expected statistically significant correlations with gestational age at birth and birthweight. Furthermore, the proposed method obtains statistically significant improvements in Dice scores when compared to the a Maximum Likelihood EM algorithm.

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A dynamic 4D probabilistic atlas of the developing brain

Cited by 252 publications

References 61 publications

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching

Adaptive Neonate Brain Segmentation

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