Ms. Yaping Wang Profile

Yaping Wang

Research Assistant at Northwestern Polytechnical Univ

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Image segmentation and classification

Publications (2)

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Proceedings Article | 13 March 2013 Paper

Mapping longitudinal cerebral cortex development using diffusion tensor imaging

Yaping Wang, Gang Li, Mihye Ahn, Jingxin Nie, Hongtu Zhu, Lei Guo

Proceedings Volume 8669, 86692E (2013) https://doi.org/10.1117/12.2007164

KEYWORDS: Brain, Cerebral cortex, Diffusion tensor imaging, Diffusion, Anisotropy, Neurons, Tissues, Neuroimaging, Statistical analysis, Statistical modeling

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Diffusion tensor imaging (DTI) could provide convenient and crucial insights into the underlying age-related biological maturation of human brains, including myelination, axonal density changes, fiber tract reorganization, and synaptic pruning processes. Fractional anisotropy (FA) derived from DTI has been commonly used to characterize cellular morphological changes associated with the development of human brain, due to its sensitivity to microstructural changes. In this paper, we aim to discern the longitudinal neurodevelopmental patterns in typically maturing human brains using 200 healthy subjects from 5 to 22 years of age, based on the FA in cortical gray matter (GM). Specifically, FA image is first aligned with the corresponding T1 image, which has been parcellated into different cortical ROIs, and then the average FA in each ROI is computed. Linear mixed model is used to analyze the FA developmental pattern in each cortical ROI. The developmental trajectory of FA in each ROI across ages is delineated, and the best-fitting models of age-related changes in FA were linear for all ROIs. FA generally increases with the age from 5 to 22 years of age. In addition, males and females follow the similar pattern, with the FA of females being generally lower than that of males in most ROIs. This provides us some insights into the microstructural changes in the longitudinal cerebral cortex development.

Proceedings Article | 13 March 2013 Paper

Consistent 4D brain extraction of serial brain MR images

Yaping Wang, Gang Li, Jingxin Nie, Pew-Thian Yap, Lei Guo, Dinggang Shen

Proceedings Volume 8669, 86693I (2013) https://doi.org/10.1117/12.2006651

KEYWORDS: Brain, Neuroimaging, Magnetic resonance imaging, Skull, Brain mapping, Image registration, Transform theory, 3D metrology, 3D image processing, Tissues

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Accurate and consistent skull stripping of serial brain MR images is of great importance in longitudinal studies that aim to detect subtle brain morphological changes. To avoid inconsistency and the potential bias introduced by independently performing skull-stripping for each time-point image, we propose an effective method that is capable of skull-stripping serial brain MR images simultaneously. Specifically, all serial images of the same subject are first affine aligned in a groupwise manner to a common space to avoid any potential bias introduced by asymmetric transforms. A brain probability map, which encapsulates prior information gathered from a population of real brain MR images, is then warped to the aligned serial images for guiding skull-stripping via a deformable surface method. In particular, the same initial surface meshes representing the initial brain surfaces are first placed on all aligned serial images, and then all these surface meshes are simultaneously evolved to the respective target brain boundaries, driven by the intensity-based force, the force from the probability map, as well as the force from the spatial and temporal smoothness. Especially, imposing the temporal smoothness helps achieve longitudinally consistent results. Evaluations on 20 subjects, each with 4 time points, from the ADNI database indicate that our method gives more accurate and consistent result compared with 3D skull-stripping method. To better show the advantages of our 4D brain extraction method over the 3D method, we compute the Dice ratio in a ring area (±5mm) surrounding the ground-truth brain boundary, and our 4D method achieves around 3% improvement over the 3D method. In addition, our 4D method also gives smaller mean and maximal surface-to- surface distance measurements, with reduced variances.

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