The Experts below are selected from a list of 18816 Experts worldwide ranked by ideXlab platform
Dinggang Shen - One of the best experts on this subject based on the ideXlab platform.
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construction of spatiotemporal neonatal cortical surface Atlases using a large scale dataset
International Symposium on Biomedical Imaging, 2018Co-Authors: Li Wang, John H Gilmore, Weili Lin, Dinggang ShenAbstract:The cortical surface Atlases constructed from a large representative population of neonates are highly needed in the neonatal neuroimaging studies. However, existing neonatal cortical surface Atlases are typically constructed from small datasets, e.g., tens of subjects, which are inherently biased and thus are not representative to the neonatal population. In this paper, we construct neonatal cortical surface Atlases based on a large-scale dataset with 764 subjects. To better characterize the dynamic cortical development during the first postnatal weeks, instead of constructing just a single atlas, we construct a set of spatiotemporal Atlases at each week from 39 to 44 gestational weeks. The central idea is that, for all cortical surfaces, we first group-wisely register them into the common space to ensure the unbiasedness. Then, rather than simply averaging over the co-registered cortical surfaces, which generally leads to over-smoothed cortical folding patterns, we adopt a spherical patch-based sparse representation using an augmented dictionary to overcome the noises and potential registration errors. Through the group-wise sparsity constraint, we obtain consistent geometric cortical folding attributes on the Atlases. Our Atlases preserve the sharp cortical folding patterns, thus leading to better registration accuracy when aligning new subjects onto the Atlases.
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detail preserving construction of neonatal brain Atlases in space frequency domain
Human Brain Mapping, 2016Co-Authors: Yuyao Zhang, Dinggang ShenAbstract:: Brain Atlases are commonly utilized in neuroimaging studies. However, most brain Atlases are fuzzy and lack structural details, especially in the cortical regions. This is mainly caused by the image averaging process involved in atlas construction, which often smoothes out high-frequency contents that capture fine anatomical details. Brain atlas construction for neonatal images is even more challenging due to insufficient spatial resolution and low tissue contrast. In this paper, we propose a novel framework for detail-preserving construction of population-representative Atlases. Our approach combines spatial and frequency information to better preserve image details. This is achieved by performing atlas construction in the space-frequency domain given by wavelet transform. In particular, sparse patch-based atlas construction is performed in all frequency subbands, and the results are combined to give a final atlas. For enhancing anatomical details, tissue probability maps are also used to guide atlas construction. Experimental results show that our approach can produce Atlases with greater structural details than existing Atlases. Hum Brain Mapp 37:2133-2150, 2016. © 2016 Wiley Periodicals, Inc.
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space frequency detail preserving construction of neonatal brain Atlases
Medical Image Computing and Computer-Assisted Intervention, 2015Co-Authors: Yuyao Zhang, Dinggang ShenAbstract:Brain Atlases are an integral component of neuroimaging studies. However, most brain Atlases are fuzzy and lack structural details, especially in the cortical regions. In particular, neonatal brain Atlases are especially challenging to construct due to the low spatial resolution and low tissue contrast. This is mainly caused by the image averaging process involved in atlas construction, often smoothing out high-frequency contents that indicate fine anatomical details. In this paper, we propose a novel framework for detail-preserving construction of Atlases. Our approach combines space and frequency information to better preserve image details. This is achieved by performing reconstruction in the space-frequency domain given by wavelet transform. Sparse patch-based atlas reconstruction is performed in each frequency subband. Combining the results for all these subbands will then result in a refined atlas. Compared with existing Atlases, experimental results indicate that our approach has the ability to build an atlas with more structural details, thus leading to better performance when used to normalize a group of testing neonatal images.
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Construction of 4D high-definition cortical surface Atlases of infants: Methods and applications
Medical image analysis, 2015Co-Authors: Li Wang, John H Gilmore, Feng Shi, Weili Lin, Dinggang ShenAbstract:In neuroimaging, cortical surface Atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface Atlases created for adults are not suitable for infant brains during the first two postnatal years, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface Atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface Atlases for the dynamic developing infant cortical structures at seven time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface Atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface Atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface Atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface Atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface Atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development.
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infant brain Atlases from neonates to 1 and 2 year olds
PLOS ONE, 2011Co-Authors: Feng Shi, John H Gilmore, Weili Lin, Pew Thian Yap, Hongjun Jia, Dinggang ShenAbstract:Background Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size. Methodology To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-year-old, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between age-specific Atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other Atlases and is hence very useful for infant-related studies. Conclusions We expect that the proposed infant 0–1–2 brain Atlases would be significantly conducive to structural and functional studies of the infant brains. These Atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.
Susumu Mori - One of the best experts on this subject based on the ideXlab platform.
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resource Atlases for multi atlas brain segmentations with multiple ontology levels based on t1 weighted mri
NeuroImage, 2016Co-Authors: Can Ceritoglu, Michael Miller, Susumu Mori, Jill Chotiyanonta, Zhipeng Hou, John Hsu, Timothy BrownAbstract:Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of Atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4-82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate Atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which Atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation.
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magnetic resonance imaging and micro computed tomography combined atlas of developing and adult mouse brains for stereotaxic surgery
Neuroscience, 2009Co-Authors: Manisha Aggarwal, Michael Miller, Jiangyang Zhang, Richard L Sidman, Susumu MoriAbstract:Stereotaxic Atlases of the mouse brain are important in neuroscience research for targeting of specific internal brain structures during surgical operations. The effectiveness of stereotaxic surgery depends on accurate mapping of the brain structures relative to landmarks on the skull. During postnatal development in the mouse, rapid growth-related changes in the brain occur concurrently with growth of bony plates at the cranial sutures, therefore adult mouse brain Atlases cannot be used to precisely guide stereotaxis in developing brains. In this study, three-dimensional stereotaxic Atlases of C57BL/6J mouse brains at six postnatal developmental stages: postnatal day (P) 7, P14, P21, P28, P63 and in adults (P140–P160) were developed, using diffusion tensor imaging (DTI) and micro-computed tomography (CT). At present, most widely-used stereotaxic Atlases of the mouse brain are based on histology, but the anatomical fidelity of ex vivo Atlases to in vivo mouse brains has not been evaluated previously. To account for ex vivo tissue distortion due to fixation as well as individual variability in the brain, we developed a population-averaged in vivo magnetic resonance imaging adult mouse brain stereotaxic atlas, and a distortion-corrected DTI atlas was generated by nonlinearly warping ex vivo data to the population-averaged in vivo atlas. These atlas resources were developed and made available through a new software user-interface with the objective of improving the accuracy of targeting brain structures during stereotaxic surgery in developing and adult C57BL/6J mouse brains.
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stereotaxic white matter atlas based on diffusion tensor imaging in an icbm template
NeuroImage, 2008Co-Authors: Susumu Mori, Kenichi Oishi, Hangyi Jiang, Li Jiang, Xin Li, Kazi Akhter, Andreia V Faria, Asif Mahmood, Roger P WoodsAbstract:Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic Atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter specific Atlases in stereotaxic coordinates are introduced. As a reference template, the widely-used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and nonlinear transformation was measured, and automated template-based white matter parcellation was tested. The results showed high correlation between the manual ROI-based and the automated approaches for normal adult populations. The Atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.
Susan M. Resnick - One of the best experts on this subject based on the ideXlab platform.
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Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography
Neuroinformatics, 2020Co-Authors: Colin B Hansen, Qi Yang, Ilwoo Lyu, Francois Rheault, Cailey Kerley, Bramsh Qamar Chandio, Shreyas Fadnavis, Owen Williams, Andrea T. Shafer, Susan M. ResnickAbstract:Brain Atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain Atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter Atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate “regions” rather than as white matter “bundles” or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter Atlases represented in both volumetric and surface coordinates in a standard space. These Atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different automated state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.
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pandora 4 d white matter bundle population based Atlases derived from diffusion mri fiber tractography
bioRxiv, 2020Co-Authors: Colin B Hansen, Qi Yang, Ilwoo Lyu, Francois Rheault, Cailey Kerley, Bramsh Qamar Chandio, Shreyas Fadnavis, Andrea T. Shafer, Owen A Williams, Susan M. ResnickAbstract:Abstract Brain Atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain Atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter Atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate “regions” rather than as white matter “bundles” or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter Atlases represented in both volumetric and surface coordinates in a standard space. These Atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.
Alexander Hammers - One of the best experts on this subject based on the ideXlab platform.
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multi atlas based segmentation of brain images atlas selection and its effect on accuracy
NeuroImage, 2009Co-Authors: Paul Aljabar, Alexander Hammers, Rolf A Heckemann, Joseph V Hajnal, Daniel RueckertAbstract:Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple Atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of Atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 Atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of Atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting Atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting Atlases by age that demonstrate the value of meta-information for selection.
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automatic segmentation of brain mris of 2 year olds into 83 regions of interest
NeuroImage, 2008Co-Authors: Ioannis S Gousias, Alexander Hammers, Rolf A Heckemann, Daniel Rueckert, James P Boardman, Leigh Dyet, David A EdwardsAbstract:Abstract Three-dimensional Atlases and databases of the brain at different ages facilitate the description of neuroanatomy and the monitoring of cerebral growth and development. Brain segmentation is challenging in young children due to structural differences compared to adults. We have developed a method, based on established algorithms, for automatic segmentation of young children's brains into 83 regions of interest (ROIs), and applied this to an exemplar group of 33 2-year-old subjects who had been born prematurely. The algorithm uses prior information from 30 normal adult brain magnetic resonance (MR) images, which had been manually segmented to create 30 Atlases, each labeling 83 anatomical structures. Each of these adult Atlases was registered to each 2-year-old target MR image using non-rigid registration based on free-form deformations. Label propagation from each adult atlas yielded a segmentation of each 2-year-old brain into 83 ROIs. The final segmentation was obtained by combination of the 30 propagated adult Atlases using decision fusion, improving accuracy over individual propagations. We validated this algorithm by comparing the automatic approach with three representative manually segmented volumetric regions (the subcortical caudate nucleus, the neocortical pre-central gyrus and the archicortical hippocampus) using similarity indices (SI), a measure of spatial overlap (intersection over average). SI results for automatic versus manual segmentations for these three structures were 0.90 ± 0.01, 0.90 ± 0.01 and 0.88 ± 0.03 respectively. This registration approach allows the rapid construction of automatically labelled age-specific brain Atlases for children at the age of 2 years.
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Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe
Human Brain Mapping, 2003Co-Authors: Alexander Hammers, Richard Allom, Tejal N. Mitchell, Ralph Myers, S L Free, Louis Lemieux, Matthias J. Koepp, David J. Brooks, John S DuncanAbstract:Probabilistic Atlases of neuroanatomy are more representative of population anatomy than single brain Atlases. They allow anatomical labeling of the results of group studies in stereotaxic space, automated anatomical labeling of individual brain imaging datasets, and the statistical assessment of normal ranges for structure volumes and extents. No such manually constructed atlas is currently available for the frequently studied group of young adults. We studied 20 normal subjects (10 women, median age 31 years) with high-resolution magnetic resonance imaging (MRI) scanning. Images were nonuniformity corrected and reoriented along both the anterior-posterior commissure (AC-PC) line horizontally and the midsagittal plane sagittally. Building on our previous work, we have expanded and refined existing algorithms for the subdivision of MRI datasets into anatomical structures. The resulting algorithm is presented in the Appendix. Forty-nine structures were interactively defined as three-dimensional volumes-of-interest (VOIs). The resulting 20 individual Atlases were spatially transformed (normalized) into standard stereotaxic space, using SPM99 software and the MNI/ICBM 152 template. We evaluated volume data for all structures both in native space and after spatial normalization, and used the normalized superimposed Atlases to create a maximum probability map in stereotaxic space, which retains quantitative information regarding inter-subject variability. Its potential applications range from the automatic labeling of new scans to the detection of anatomical abnormalities in patients. Further data can be extracted from the atlas for the detailed analysis of individual structures.
Helene Benveniste - One of the best experts on this subject based on the ideXlab platform.
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in vivo 3d digital atlas database of the adult c57bl 6j mouse brain by magnetic resonance microscopy
Frontiers in Neuroanatomy, 2008Co-Authors: David Smith, Stephen J Blackband, Patrick R Hof, Helene Benveniste, Bernd Foerster, Scott HamiltonAbstract:In this study, a 3D digital atlas of the live mouse brain based on magnetic resonance microscopy (MRM) is presented. C57BL/6J adult mouse brains were imaged in vivo on a 9.4 Tesla MR instrument at an isotropic spatial resolution of 100 μm. With sufficient signal-to-noise (SNR) and contrast-to-noise ratio (CNR), 20 brain regions were identified. Several Atlases were constructed including 12 individual brain Atlases, an average atlas, a probabilistic atlas and average geometrical deformation maps. We also investigated the feasibility of using lower spatial resolution images to improve time efficiency for future morphological phenotyping. All of the new in vivo data were compared to previous published in vitro C57BL/6J mouse brain Atlases and the morphological differences were characterized. Our analyses revealed significant volumetric as well as unexpected geometrical differences between the in vivo and in vitro brain groups which in some instances were predictable (e.g. collapsed and smaller ventricles in vitro but not in other instances. Based on these findings we conclude that although in vitro datasets, compared to in vivo images, offer higher spatial resolutions, superior SNR and CNR, leading to improved image segmentation, in vivo Atlases are likely to be an overall better geometric match for in vivo studies, which are necessary for longitudinal examinations of the same animals and for functional brain activation studies. Thus the new in vivo mouse brain atlas dataset presented here is a valuable complement to the current mouse brain atlas collection and will be accessible to the neuroscience community on our public domain mouse brain atlas website.
