A Neonatal Bimodal MR-CT Head Template

Ghadimi, Sona; Mohtasebi, Mehrana; Moghaddam, Hamid Abrishami; Grebe, Reinhard; Gity, Masoumeh; Wallois, Fabrice

doi:10.1371/journal.pone.0166112

Cited by 9 publications

(17 citation statements)

References 57 publications

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“…The same research group also provided a symmetric template built from all 14 term-born neonates for asymmetry studies of language development (Noorizadeh et al, 2013). Recently, a bimodal neonatal brain template consisting of an average magnetic resonance (MR) and computed tomography (CT) image was added (Ghadimi et al, 2017).…”

Section: Brain Atlas Constructionmentioning

confidence: 99%

Unbiased construction of a temporally consistent morphological atlas of neonatal brain development

Schuh¹,

A²,

et al. 2018

Preprint

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Premature birth increases the risk of developing neurocognitive and neurobehavioural disorders. The mechanisms of altered brain development causing these disorders are yet unknown. Studying the morphology and function of the brain during maturation provides us not only with a better understanding of normal development, but may help us to identify causes of abnormal development and their consequences. A particular difficulty is to distinguish abnormal patterns of neurodevelopment from normal variation. The Developing Human Connectome Project (dHCP) seeks to create a detailed four-dimensional (4D) connectome of early life. This connectome may provide insights into normal as well as abnormal patterns of brain development.As part of this project, more than a thousand healthy fetal and neonatal * Corresponding authorEmail address: andreas.schuh.84@gmail.com (Andreas Schuh)Preprint submitted to NeuroImage January 28, 2018peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/251512 doi: bioRxiv preprint first posted online brains will be scanned in vivo. This requires computational methods which scale well to larger data sets. We propose a novel groupwise method for the construction of a spatio-temporal model of mean morphology from crosssectional brain scans at different gestational ages. This model scales linearly with the number of images and thus improves upon methods used to build existing public neonatal atlases, which derive correspondence between all pairs of images. By jointly estimating mean shape and longitudinal change, the atlas created with our method overcomes temporal inconsistencies, which are encountered when mean shape and intensity images are constructed separately for each time point. Using this approach, we have constructed a spatio-temporal atlas from 275 healthy neonates between 35 and 44 weeks post-menstrual age (PMA). The resulting atlas qualitatively preserves cortical details significantly better than publicly available atlases. This is moreover confirmed by a number of quantitative measures of the quality of the spatial normalisation and sharpness of the resulting template brain images.Keywords: dHCP, spatio-temporal, neonatal MRI, brain atlas 2 peer-reviewed)

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Section: Brain Atlas Constructionmentioning

confidence: 99%

Unbiased construction of a temporally consistent morphological atlas of neonatal brain development

Schuh¹,

A²,

et al. 2018

Preprint

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“…The images used in this study were exactly the same images as used to build the CT atlas in (Ghadimi et al, 2015; Ghadimi et al, 2017; Mohtasebi et al, 2021). The dataset contains 16 CT images including 12 boys and 4 girls of infants aged from 39 to 42 weeks at the date of examination (GA).…”

Section: Methodsmentioning

confidence: 99%

Skull and scalp segmentation in neonatal cerebral MRI using subject-specific probability models

Hokmabadi

Moghaddam

Mohtasebi

et al. 2022

Preprint

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This study presents a new approach for the segmentation of cranial bones in magnetic resonance images (MRIs) acquired from neonates in the age range of 39 to 42 weeks, gestational age. This approach uses subject specific probability maps of the skull and scalp, which are created from atlas computed tomography (CT) images taken retrospectively from neonates in the same age range. Our method uses also a subject specific probability map of cerebrospinal fluid (CSF), which is constructed from retrospective atlas MRIs. To construct skull, scalp and CSF probability maps, a subject specific bimodal MR-CT neonatal head template is created (using atlas MR and CT images), and employed. In the next step, the subject specific probability maps are fed to expectation maximization algorithm in conjunction with Markov random field method implemented in FSL software to segment skull and scalp from the input MR image. The results of the proposed method were evaluated through various experiments. First, we computed the similarity between frontal and occipital sutures (reconstructed from segmented cranial bones) and the ground truth (created manually by an expert radiologist). For this purpose, modified versions of Dice similarity index (DSI) were adopted and used. Second, the size of anterior fontanel was compared to its normal size as reported for the neonates in the same age range. Third, the thickness of cranial bones was computed and compared to its normal values as reported for healthy neonates. Finally, a retrospective data including MRI and CT images was used which have been acquired from the same neonate within a short time interval. After aligning the two images, the similarity between cranial bones of the MR and CT image was compared using DSI and modified Hausdorff distance. The results of these experiments demonstrated the success of our segmentation method.

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“…The method of seed delivery is often applied to sometime varying biological signs, such as face, etc., and it can also be used to improve some permanent changes of biological modes, such as palmprint with scar marks. Template improvement is based on comparing old and new templates to remove redundant features and increasing the feature dimension of templates to enrich template information and perfect template details [40].…”

Section: Feature Layer Fusionmentioning

confidence: 99%

“…At present, there are many hierarchical fusion algorithms that can solve the above problems. They can be further divided into: transform based score fusion method, classifier-based score fusion method and density-based score fusion method [40][41][42][43][44][45].…”

Section: Knowledge Layer Fusionmentioning

confidence: 99%

Interdisciplinary Evolution of the Machine Brain

Wang

Cai

Deng

et al. 2021

Interdisciplinary Evolution of the Machine Brain

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This chapter finally presents a characterization of interdisciplinary evolution of the machine brain. Perspective schemes for rebuilding a real vision brain in the future are analyzed, along with the major principles to construct the machine brain, are presented, which include memory, thinking, imagination, feeling, speaking and other aspects associated with machine vision, machine touch and machine minds. This explicitly developed the theoretical framework of brain-inspired intelligence from the vision brain hypothesis, the vision-minds hypothesis and the skin brain hypothesis. Based on Chaps. 2-5, development of machine intelligence during the past decades have experienced three stages-machine computation, learning and understanding. Machine leaning includes data mining. Environmental sensing helps to acquire the data. Pattern analysis and scene understanding are significant parts of machine understanding. Scientists have taught machine how to collect and treat data and discover knowledge from the data. Evolution of machine brain will experience another two stages-machine meta-learning (learning to learn) and selfdirected development (improving the capability of machine brain utilizing the learned knowledge). There are still great challenges in realization of the dream.

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A Neonatal Bimodal MR-CT Head Template

Cited by 9 publications

References 57 publications

Unbiased construction of a temporally consistent morphological atlas of neonatal brain development

Unbiased construction of a temporally consistent morphological atlas of neonatal brain development

Skull and scalp segmentation in neonatal cerebral MRI using subject-specific probability models

Interdisciplinary Evolution of the Machine Brain

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