A Spatio-temporal Atlas of the Human Fetal Brain with Application to Tissue Segmentation

Habas, Piotr A.; Kim, Kio; Rousseau, François; Glenn, Orit A.; Barkovich, A. James; Studholme, Colin

doi:10.1007/978-3-642-04268-3_36

Cited by 11 publications

(13 citation statements)

References 12 publications

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“…In terms of application, this new approach could be useful in the case of lower contrast imaging (limited by the imaging time, or challenged by inherently low contrast tissue boundaries) for example in the study of the developing human foetus [44].…”

Section: Resultsmentioning

confidence: 99%

A non-local fuzzy segmentation method: Application to brain MRI

Caldairou

Passat

Habas

et al. 2011

Pattern Recognition

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116

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a b s t r a c tThe Fuzzy C-Means (FCM) algorithm is a widely used and flexible approach to automated image segmentation, especially in the field of brain tissue segmentation from 3D MRI, where it addresses the problem of partial volume effects. In order to improve its robustness to classical image deterioration, namely noise and bias field artifacts, which arise in the MRI acquisition process, we propose to integrate into the FCM segmentation methodology concepts inspired by the non-local (NL) framework, initially defined and considered in the context of image restoration. The key algorithmic contributions of this article are the definition of an NL data term and an NL regularisation term to efficiently handle intensity inhomogeneities and noise in the data. The resulting new energy formulation is then built into an NL-FCM brain tissue segmentation algorithm. Experiments performed on both synthetic and real MRI data, leading to the classification of brain tissues into grey matter, white matter and cerebrospinal fluid, indicate a significant improvement in performance in the case of higher noise levels, when compared to a range of standard algorithms.

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Section: Resultsmentioning

confidence: 99%

A non-local fuzzy segmentation method: Application to brain MRI

Caldairou

Passat

Habas

et al. 2011

Pattern Recognition

Self Cite

116

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“…To relate manual delineations of multiple subjects and build the probabilistic atlas, we created an unbiased linear average shape and intensity model from the available set of training subjects. Although registration of reconstructed MR images was overall accurate, we are currently working on an approach where existing manual segmentations of subject volumes rather than MR images are collectively registered to create a reference anatomy [Habas et al, 2009a]. This may help us resolve some registration ambiguities between different tissues that appear with very similar intensities in relatively low-contrast clinical MR scans.…”

Section: Discussionmentioning

confidence: 99%

Atlas‐based segmentation of developing tissues in the human brain with quantitative validation in young fetuses

Habas

Kim

Rousseau

et al. 2010

Human Brain Mapping

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Imaging of the human fetus using magnetic resonance (MR) is an essential tool for quantitative studies of normal as well as abnormal brain development in utero. However, because of fundamental differences in tissue types, tissue properties and tissue distribution between the fetal and adult brain, automated tissue segmentation techniques developed for adult brain anatomy are unsuitable for this data. In this paper, we describe methodology for automatic atlas-based segmentation of individual tissue types in motion-corrected 3D volumes reconstructed from clinical MR scans of the fetal brain. To generate anatomically correct automatic segmentations, we create a set of accurate manual delineations and build an in utero 3D statistical atlas of tissue distribution incorporating developing grey and white matter as well as transient tissue types such as the germinal matrix. The probabilistic atlas is associated with an unbiased average shape and intensity template for registration of new subject images to the space of the atlas. Quantitative whole brain 3D validation of tissue labeling performed on a set of 14 fetal MR scans (20.57–22.86 weeks gestational age) demonstrates that this atlas-based EM segmentation approach achieves consistently high DSC performance for the main tissue types in the fetal brain. This work indicates that reliable measures of brain development can be automatically derived from clinical MR imaging and opens up possibility of further 3D volumetric and morphometric studies with multiple fetal subjects.

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“…There have been a few magnetic resonance imaging (MRI) studies on the hippocampus in the fetal population, although these have been limited to two dimensional assessments in postmortem fetuses (12, 14) or in vivo fetuses (12, 14). With recent advances in image processing methods (15-17), 3D segmentation of the fetal hippocampus can now be performed using in utero MR (magnetic resonance) images.…”

Section: Introductionmentioning

confidence: 99%

Fetal Hippocampal Development: Analysis by Magnetic Resonance Imaging Volumetry

et al. 2011

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The hippocampal formation plays an important role in learning and memory, however data on its development in utero in humans is limited. This study was performed to evaluate hippocampal development in healthy fetuses using 3D reconstructed magnetic resonance imaging (MRI). A cohort of 20 healthy pregnant women underwent prenatal MRI at a median gestational age of 24.9 weeks (range 21.3-31.9 weeks); 6 of the women also had a second fetal MRI performed at a 6 week interval. Routine 2D ultrafast T2-weighted images were used to reconstruct a 3D volume image, which was then used to manually segment the right and left hippocampi. Total hippocampal volume was calculated for each subject and compared against gestational age. There was a linear increase in total hippocampal volume with increasing gestational age (P<0.001). For subjects scanned twice, there was an increase in hippocampal size on the second fetal MRI (P=0.0004). This represents the first volumetric study of fetal hippocampal development in vivo. This normative volumetric data will be helpful for future comparison studies of suspected developmental abnormalities of hippocampal structure and function.

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A Spatio-temporal Atlas of the Human Fetal Brain with Application to Tissue Segmentation

Cited by 11 publications

References 12 publications

A non-local fuzzy segmentation method: Application to brain MRI

A non-local fuzzy segmentation method: Application to brain MRI

Atlas‐based segmentation of developing tissues in the human brain with quantitative validation in young fetuses

Fetal Hippocampal Development: Analysis by Magnetic Resonance Imaging Volumetry

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