Automatic cortical sulcal parcellation based on surface principal direction flow field tracking

Li, Gang; Guo, Lei; Nie, Jingxin; Liu, Tianming

doi:10.1016/j.neuroimage.2009.03.039

Cited by 64 publications

(95 citation statements)

References 86 publications

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“…While volumetric atlases are suitable for studying subcortical structures, cortical surface atlases enable better studying of the highly-convoluted and highly-variable cerebral cortex (Van Essen and Dierker, 2007). This is because cortical surface-based analysis, which explicitly reconstructs surface mesh representations of the highly-folded cerebral cortex, respects the intrinsic topological properties of the cortex and thus greatly facilitates the spatial normalization, analysis, comparison, and visualization of convoluted cortical regions (Fischl et al, 1999b; Goebel et al, 2006; Han et al, 2004; Li et al, 2009, 2010a; MacDonald et al, 2000; Mangin et al, 2004; Nie et al, 2007; Shattuck and Leahy, 2002; Shi et al, 2013; Shiee et al, 2014; Van Essen and Dierker, 2007; Xu et al, 1999). Moreover, cortical surface-based measurements, e.g., surface area (Hill et al, 2010b), cortical thickness (Fischl and Dale, 2000), and cortical folding/gyrification (Habas et al, 2012; Li et al, 2010b; Rodriguez-Carranza et al, 2008; Zhang et al, 2009; Zilles et al, 2013), each with distinct genetic underpinning, cellular mechanism, and developmental trajectory (Chen et al, 2013; Lyall et al, 2014; Panizzon et al, 2009), can comprehensively provide various detailed aspects of the cerebral cortex (Li et al, 2014a).…”

Section: Introductionmentioning

confidence: 99%

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

Wang

Shi

et al. 2015

Medical Image Analysis

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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 years of life, 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 7 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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Section: Introductionmentioning

confidence: 99%

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

Wang

Shi

et al. 2015

Medical Image Analysis

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121

116

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“…In particular, the accurate segmentation of cortical surfaces into major folds, or sulcal areas, is fundamental to many applications involving region-specific measurements. Two strategies exist for cortical parcellation and are either template based [1][2][3][4][5][6][7], via iterative deformations of a pre-labeled atlas, or subject based, via costly processing of sulcal data [8][9][10] or extracted sulcal lines [11][12][13]. Present methods often suffer from a heavy computational burden.…”

Section: Introductionmentioning

confidence: 99%

Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation

Lombaert¹,

Criminisi²,

Ayache³

2015

Lecture Notes in Computer Science

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“…However, manual parcellation of the highly-folded cortical surface is extremely tedious, time-consuming, and subject to inter-rater variation. Accordingly, many methods have been proposed for cortical surface parcellation in the cross-sectional adult studies, based on the sulcal-gyral folding geometries from structural MR images (Cachia et al, 2003; Desikan et al, 2006; Destrieux et al, 2010; Fischl et al, 2004; Hu et al, 2010; Joshi et al, 2012; Klein and Hirsch, 2005; Klein and Tourville, 2012; Li et al, 2009; Li et al, 2013c; Liu et al, 2004; Lohmann and von Cramon, 2000; Nie et al, 2007; Rettmann et al, 2002; Shi et al, 2013; Van Essen et al, 2012; Wan et al, 2008; Yang and Kruggel, 2008; Yeo et al, 2008; Zhang et al, 2010). …”

Section: Introductionmentioning

confidence: 99%

Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants

Wang

Shi

et al. 2014

Medical Image Analysis

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The human cerebral cortex develops extremely dynamically in the first two years of life. Accurate and consistent parcellation of longitudinal dynamic cortical surfaces during this critical stage is essential to understand the early development of cortical structure and function in both normal and high-risk infant brains. However, directly applying the existing methods developed for the cross-sectional studies often generates longitudinally-inconsistent results, thus leading to inaccurate measurements of the cortex development. In this paper, we propose a new method for accurate, consistent, and simultaneous labeling of longitudinal cortical surfaces in the serial infant brain MR images. The proposed method is explicitly formulated as a minimization problem with an energy function that includes a data fitting term, a spatial smoothness term, and a temporal consistency term. Specifically, inspired by multi-atlas based label fusion, the data fitting term is designed to integrate the contributions from multi-atlas surfaces adaptively, according to the similarities of their local cortical folding with that of the subject cortical surface. The spatial smoothness term is then designed to adaptively encourage label smoothness based on the local cortical folding geometries, i.e. allowing label discontinuity at sulcal bottoms (which often are the boundaries of cytoarchitecturally and functionally distinct regions). The temporal consistency term is to adaptively encourage the label consistency among the temporally-corresponding vertices, based on their similarity of local cortical folding. Finally, the entire energy function is efficiently minimized by a graph cuts method. The proposed method has been applied to the parcellation of longitudinal cortical surfaces of 13 healthy infants, each with 6 serial MRI scans acquired at 0, 3, 6, 9, 12 and 18 months of age. Qualitative and quantitative evaluations demonstrated both accuracy and longitudinal consistency of the proposed method. By using our method, for the first time, we reveal several hitherto unseen properties of the dynamic and regionally heterogeneous development of the cortical surface area in the first 18 months of life.

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Automatic cortical sulcal parcellation based on surface principal direction flow field tracking

Cited by 64 publications

References 86 publications

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation

Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants

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