Author name cluster

Stephen T.C. Wong

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10 papers

1 author row

YNIMG Journal 2012 Journal Article

A quantitative analytic pipeline for evaluating neuronal activities by high‐throughput synaptic vesicle imaging

Jing Fan
Xiaofeng Xia
Ying Li
Jennifer G. Dy
Stephen T.C. Wong

Synaptic vesicle dynamics play an important role in the study of neuronal and synaptic activities of neurodegradation diseases ranging from the epidemic Alzheimer's disease to the rare Rett syndrome. A high-throughput assay with a large population of neurons would be useful and efficient to characterize neuronal activity based on the dynamics of synaptic vesicles for the study of mechanisms or to discover drug candidates for neurodegenerative and neurodevelopmental disorders. However, the massive amounts of image data generated via high‐throughput screening require enormous manual processing time and effort, restricting the practical use of such an assay. This paper presents an automated analytic system to process and interpret the huge data set generated by such assays. Our system enables the automated detection, segmentation, quantification, and measurement of neuron activities based on the synaptic vesicle assay. To overcome challenges such as noisy background, inhomogeneity, and tiny object size, we first employ MSVST (Multi-Scale Variance Stabilizing Transform) to obtain a denoised and enhanced map of the original image data. Then, we propose an adaptive thresholding strategy to solve the inhomogeneity issue, based on the local information, and to accurately segment synaptic vesicles. We design algorithms to address the issue of tiny objects of interest overlapping. Several post processing criteria are defined to filter false positives. A total of 152 features are extracted for each detected vesicle. A score is defined for each synaptic vesicle image to quantify the neuron activity. We also compare the unsupervised strategy with the supervised method. Our experiments on hippocampal neuron assays showed that the proposed system can automatically detect vesicles and quantify their dynamics for evaluating neuron activities. The availability of such an automated system will open opportunities for investigation of synaptic neuropathology and identification of candidate therapeutics for neurodegeneration.

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YNIMG Journal 2010 Journal Article

A hybrid approach to automatic clustering of white matter fibers

Hai Li
Zhong Xue
Lei Guo
Tianming Liu
Jill Hunter
Stephen T.C. Wong

Recently, the tract-based white matter (WM) fiber analysis has been recognized as an effective framework to study the diffusion tensor imaging (DTI) data of human brain. This framework can provide biologically meaningful results and facilitate the tract-based comparison across subjects. However, due to the lack of quantitative definition of WM bundle boundaries, the complexity of brain architecture and the variability of WM shapes, clustering WM fibers into anatomically meaningful bundles is nontrivial. In this paper, we propose a hybrid top-down and bottom-up approach for automatic clustering and labeling of WM fibers, which utilizes both brain parcellation results and similarities between WM fibers. Our experimental results show reasonably good performance of this approach in clustering WM fibers into anatomically meaningful bundles.

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YNIMG Journal 2010 Journal Article

A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation

Zhong Xue
Hai Li
Lei Guo
Stephen T.C. Wong

It is a key step to spatially align diffusion tensor images (DTI) to quantitatively compare neural images obtained from different subjects or the same subject at different timepoints. Different from traditional scalar or multi-channel image registration methods, tensor orientation should be considered in DTI registration. Recently, several DTI registration methods have been proposed in the literature, but deformation fields are purely dependent on the tensor features not the whole tensor information. Other methods, such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms, use analytical gradients of the registration objective functions by simultaneously considering the reorientation and deformation of tensors during the registration. However, only relatively local tensor information such as voxel-wise tensor-similarity is utilized. This paper proposes a new DTI image registration algorithm, called local fast marching (FM)-based simultaneous registration. The algorithm not only considers the orientation of tensors during registration but also utilizes the neighborhood tensor information of each voxel to drive the deformation, and such neighborhood tensor information is extracted from a local fast marching algorithm around the voxels of interest. These local fast marching-based tensor features efficiently reflect the diffusion patterns around each voxel within a spherical neighborhood and can capture relatively distinctive features of the anatomical structures. Using simulated and real DTI human brain data the experimental results show that the proposed algorithm is more accurate compared with the FA-based registration and is more efficient than its counterpart, the neighborhood tensor similarity-based registration.

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YNIMG Journal 2010 Journal Article

A neurocomputational method for fully automated 3D dendritic spine detection and segmentation of medium-sized spiny neurons

Yong Zhang
Kun Chen
Matthew Baron
Merilee A. Teylan
Yong Kim
Zhihuan Song
Paul Greengard
Stephen T.C. Wong

Acquisition and quantitative analysis of high resolution images of dendritic spines are challenging tasks but are necessary for the study of animal models of neurological and psychiatric diseases. Currently available methods for automated dendritic spine detection are for the most part customized for 2D image slices, not volumetric 3D images. In this work, a fully automated method is proposed to detect and segment dendritic spines from 3D confocal microscopy images of medium-sized spiny neurons (MSNs). MSNs constitute a major neuronal population in striatum, and abnormalities in their function are associated with several neurological and psychiatric diseases. Such automated detection is critical for the development of new 3D neuronal assays which can be used for the screening of drugs and the studies of their therapeutic effects. The proposed method utilizes a generalized gradient vector flow (GGVF) with a new smoothing constraint and then detects feature points near the central regions of dendrites and spines. Then, the central regions are refined and separated based on eigen-analysis and multiple shape measurements. Finally, the spines are segmented in 3D space using the fast marching algorithm, taking the detected central regions of spines as initial points. The proposed method is compared with three popular existing methods for centerline extraction and also with manual results for dendritic spine detection in 3D space. The experimental results and comparisons show that the proposed method is able to automatically and accurately detect, segment, and quantitate dendritic spines in 3D images of MSNs.

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YNIMG Journal 2008 Journal Article

Reconstruction of central cortical surface from brain MRI images: Method and application

Tianming Liu
Jingxin Nie
Ashley Tarokh
Lei Guo
Stephen T.C. Wong

Reconstruction of the central surface representation of the cerebral cortex is an important means to study the structure and function of the human brain. In this paper, we propose a novel method based on an elastic transform vector field to drive a deformable model for the reconstruction of the central cortical surface. Both simulated brain cortexes and real brain images are used to evaluate this approach. We applied the surface reconstruction method and a hybrid volumetric and surface registration algorithm to detect simulated brain atrophy. Experimental results show that the central cortical surface representation has better performance in detecting simulated atrophy than the traditionally used inner or outer cortical surface representations.

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YNIMG Journal 2007 Journal Article

Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays

Yong Zhang
Xiaobo Zhou
Alexei Degterev
Marta Lipinski
Donald Adjeroh
Junying Yuan
Stephen T.C. Wong

High-throughput screening (HTS) of cell-based assays has recently emerged as an important tool of drug discovery. The analysis and modeling of HTS microscopy neuron images, however, is particularly challenging. In this paper we present a novel algorithm for extraction and quantification of neurite segments from HTS neuron images. The algorithm is designed to be able to detect and link neurites even with complex neuronal structures and of poor imaging quality. Our proposed algorithm automatically detects initial seed points on a set of grid lines and estimates the ending points of the neurite by iteratively tracing the centerline points along the line path representing the neurite segment. The live-wire method is then applied to link the seed points and the corresponding ending points using dynamic programming techniques, thus enabling the extraction of the centerlines of the neurite segments accurately and robustly against noise, discontinuity, and other image artifacts. A fast implementation of our algorithm using dynamic programming is also provided in the paper. Any thin neurite and its segments with low intensity contrast can be well preserved by detecting the starting and ending points of the neurite. All these properties make the proposed algorithm attractive for high-throughput screening of neuron-based assays.

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YNIMG Journal 2007 Journal Article

Brain tissue segmentation based on DTI data

Tianming Liu
Hai Li
Kelvin Wong
Ashley Tarokh
Lei Guo
Stephen T.C. Wong

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.

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YNIMG Journal 2007 Journal Article

Dendritic spine detection using curvilinear structure detector and LDA classifier

Yong Zhang
Xiaobo Zhou
Rochelle M. Witt
Bernardo L. Sabatini
Donald Adjeroh
Stephen T.C. Wong

Dendritic spines are small, bulbous cellular compartments that carry synapses. Biologists have been studying the biochemical pathways by examining the morphological and statistical changes of the dendritic spines at the intracellular level. In this paper a novel approach is presented for automated detection of dendritic spines in neuron images. The dendritic spines are recognized as small objects of variable shape attached or detached to multiple dendritic backbones in the 2D projection of the image stack along the optical direction. We extend the curvilinear structure detector to extract the boundaries as well as the centerlines for the dendritic backbones and spines. We further build a classifier using Linear Discriminate Analysis (LDA) to classify the attached spines into valid and invalid types to improve the accuracy of the spine detection. We evaluate the proposed approach by comparing with the manual results in terms of backbone length, spine number, spine length, and spine density.

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YNIMG Journal 2006 Journal Article

76-Space analysis of grey matter diffusivity: Methods and applications

Tianming Liu
Geoffrey Young
Ling Huang
Nan-Kuei Chen
Stephen T.C. Wong

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) allow in vivo investigation of molecular motion of tissue water at a microscopic level in cerebral gray matter (GM) and white matter (WM). DWI/DTI measure of water diffusion has been proven to be invaluable for the study of many neurodegenerative diseases (e. g. , Alzheimer's disease and Creutzfeldt–Jakob disease) that predominantly involve GM. Thus, quantitative analysis of GM diffusivity is of scientific interest and is promised to have a clinical impact on the investigation of normal brain aging and neuropathology. In this paper, we propose an automated framework for analysis of GM diffusivity in 76 standard anatomic subdivisions of gray matter to facilitate studies of neurodegenerative and other gray matter neurological diseases. The computational framework includes three enabling technologies: (1) automatic parcellation of structural MRI GM into 76 precisely defined neuroanatomic subregions (“76-space”), (2) automated segmentation of GM, WM and CSF based on DTI data, and (3) automatic measurement of the average apparent diffusion coefficient (ADC) in each segmented GM subregion. We evaluate and validate this computational framework for 76-space GM diffusivity analysis using data from normal volunteers and from patients with Creutzfeldt–Jakob disease.

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YNIMG Journal 2006 Journal Article

Repulsive force based snake model to segment and track neuronal axons in 3D microscopy image stacks

Hongmin Cai
Xiaoyin Xu
Ju Lu
Jeff W. Lichtman
S.P. Yung
Stephen T.C. Wong

The branching patterns of axons and dendrites are fundamental structural properties that affect the synaptic connectivity of axons. Although today three-dimensional images of fluorescently labeled processes can be obtained to study axonal branching, there are no robust methods of tracing individual axons. This paper describes a repulsive force based snake model to segment and track axonal profiles in 3D images. This new method segments all the axonal profiles in a 2D image and then uses the results obtained from that image as prior information to help segment the adjacent 2D image. In this way, the segmentation successfully connects axonal profiles over hundreds of images in a 3D image stack. Individual axons can then be extracted based on the segmentation results. The utility and performance of the method are demonstrated using 3D axonal images obtained from transgenic mice that express fluorescent protein.

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