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Labeling Lateral Prefrontal Sulci using Spherical Data Augmentation and Context-aware Training
2021
NeuroImage
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In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional ...
We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. ...
This is a general issue in modern deep neural networks regardless of data augmentation. ...
doi:10.1016/j.neuroimage.2021.117758
pmid:33497773
pmcid:PMC8366030
fatcat:bwsbaooksvecbpqhkidgvrdtma
A new tripartite landmark in posterior cingulate cortex
[article]
2021
bioRxiv
pre-print
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Manually labeling 4,319 sulci in 552 hemispheres, we discovered a consistently localized shallow cortical indentation (termed the inframarginal sulcus; ifrms) within PCC that is absent from neuroanatomical ...
These findings support a classic theory that shallow, tertiary sulci serve as landmarks in association cortices. They also beg the question: how many other cortical indentations have we missed? ...
We used recently developed (Methods 37 ) spherical convolutional neural networks (CNNs) with context-aware training on the eight PMC sulci identified in the young adult participants. ...
doi:10.1101/2021.10.30.466521
fatcat:iq3oq2td7rhuxnxoin6d5pkm3u
Quantitative in vivo MRI measurement of cortical development in the fetus
2011
Brain Structure and Function
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We describe the in vivo fetal gyrification process using a robust feature extraction algorithm applied directly on the cortical surface, providing an explicit delineation of the sulcal pattern during fetal ...
In this study, we investigate the in vivo fetal cortical folding pattern in healthy fetuses between 25 and 35 weeks gestational age using 3-D reconstructed fetal cortical surfaces. ...
We are indebted to the families for participating in this study. ...
doi:10.1007/s00429-011-0325-x
pmid:21562906
fatcat:dmi4xxvhrvdgllzktwv5mfp2ra
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
2021
Sensors
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As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be ...
We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21144758
fatcat:jytyt4u2pjgvhnhcto3vcvd3a4
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be ...
We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. ...
The same authors also implement a spherical U-Net for cortical sulci labeling from relatively few samples in a developmental cohort [27] . ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
The need for improved brain lesion segmentation techniques for children with cerebral palsy: A review
2015
International Journal of Developmental Neuroscience
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In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions ...
The need for improved brain lesion segmentation techniques for children with cerebral palsy: a review.International Journal of Developmental Neuroscience http://dx. ...
Roslyn Boyd is supported by a NHMRC Career Development Fellowship (1037220) and a NHMRC Project Grant COMBIT (1003887). No other authors have potential conflicts of interest to declare. ...
doi:10.1016/j.ijdevneu.2015.08.004
pmid:26394278
fatcat:bphc4r2blbenhecjydluue3j5a
Mapping fetal brain development based on automated segmentation and 4D brain atlasing
[article]
2020
bioRxiv
pre-print
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First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. ...
gestation in a Chinese population. ...
Kleesiek et al. used a convolutional neural network (CNN) for adult brain extraction, with used cubic windows of a fixed size around each voxel (Kleesiek et al., 2016) . ...
doi:10.1101/2020.05.10.085381
fatcat:iqu6dayh3bgwbmuci7q4lpuhwe
Age-specific Structural Fetal Brain Atlases Construction and Cortical Development Quantification for Chinese Population
2021
NeuroImage
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In this paper, we use an unbiased template construction algorithm to create a set of age-specific Chinese fetal atlases between 21-35 weeks of gestation from 115 normal fetal brains. ...
Additionally, a direct comparison of the Chinese fetal atlases and Caucasian fetal atlases reveals dramatic anatomical differences, mainly in the medial frontal and temporal regions. ...
Briefly, a Convolutional Neural Network (CNN) was firstly used to automatically localize the fetal brain in each input LR stack to obtain an initial segmentation of the fetal brain. ...
doi:10.1016/j.neuroimage.2021.118412
pmid:34298085
fatcat:qntt24vjq5gztgncw6vckbw6yq
Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives
2019
NeuroImage
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Here, we propose to model individual variability using a distinctive keypoint signature: a set of unique, localized patterns, detected automatically in each image by a generic saliency operator. ...
Neuroimaging studies typically adopt a common feature space for all data, which may obscure aspects of neuroanatomy only observable in subsets of a population, e.g. cortical folding patterns unique to ...
Keypoint detection here is based on a recursive Gaussian filtering process that is analogous to a highly efficient, unbiased convolutional neural network (CNN) used in deep learning (LeCun et al., 2015 ...
doi:10.1016/j.neuroimage.2019.116208
pmid:31546048
pmcid:PMC6931906
fatcat:yalnyvwlbrettgz37hnd54jg3q
Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review
[article]
2021
arXiv
pre-print
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In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. ...
Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. ...
CHY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. ...
arXiv:2104.11644v1
fatcat:l3ixpcwu7jb7zcqo2sm5pswmja
The NonHuman Primate Neuroimaging & Neuroanatomy Project
2021
NeuroImage
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We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth ...
Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, 'ground truth' validation of connectivity using invasive tracers is not feasible in humans. ...
Acknowledgements
This study is supported by a grant Brain/MINDS -beyond from
References under revision of this special issue of NeuroImage Autio, J.A., Zhu, Q., Li, X., Glasser, M.F., Schwiedrzik, ...
doi:10.1016/j.neuroimage.2021.117726
pmid:33484849
pmcid:PMC8079967
fatcat:tfv3sm6xpjfnfj2uarpqwnyc5u
The NonHuman Primate Neuroimaging Neuroanatomy Project
[article]
2021
arXiv
pre-print
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We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth ...
However, ground truth validation of connectivity using invasive tracers is not feasible in humans. ...
A standardized venous map is used for creating a subject-specific venous map, which is then used for extracting the time series as a feature when automatically denoising functional MRI data using ICA+FIX ...
arXiv:2010.00308v3
fatcat:xqccnaz2hzcs7pqedcutkvktea
A collaborative resource platform for non-human primate neuroimaging
[article]
2020
biorxiv/medrxiv
pre-print
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Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. ...
Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. ...
A convolutional neural network model for skull-stripping of NHP images. ...
doi:10.1101/2020.07.31.230185
fatcat:6vpogby2ezhu3jgxnpjlkbt3ai
Modeling & Analysis
2003
NeuroImage
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For each brain, the main cortical sulci are automatically extracted and recognized using a set of processing tools freely available on "http://anatomist.info" [5]. ...
Abstract A new neural network algorithm has been developed for the automatic visual segmentation of T1-weighted 3-D head magnetic resonance images. ...
This paradigm has been extensively used in electrophysiological recordings of single cell activity in various cortical areas of non-human primates in order to characterize the relation between grip force ...
doi:10.1016/s1053-8119(05)70006-9
fatcat:zff2suxcofbxvetfrwfwcxi3zm
Connectivity-Driven Parcellation Methods for the Human Cerebral Cortex
[article]
2018
arXiv
pre-print
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Second, we cast the parcellation problem as a feature reduction problem and make use of manifold learning and image segmentation techniques to identify cortical regions with distinct structural connectivity ...
Our contributions are four-fold: First, we propose a clustering approach to delineate a cortical parcellation that provides a reliable abstraction of the brain's functional organisation. ...
Acknowledgements I would like to first of all thank my supervisor Daniel Rueckert for giving me the opportunity ...
arXiv:1802.06772v1
fatcat:zeu6okppcnbhjakwyoe3gf64pi
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