Biomedical subjects

Dinggang Shen

Publications and source records attributed to Dinggang Shen.

At least 19 recordsLinked to original sources

PubMed2006

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging.

We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

Aged↗

PubMed2006

Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms.

Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and intra-individual deformations. The paper first describes a statistical approach to capturing inter-individual variability of high-deformation fields from a number of examples (training samples). In this approach, Wavelet-Packet Transform (WPT) of the training deformations and their Jacobians, in conjunction with a Markov random field (MRF) spatial regularization, are used to capture both coarse and fine characteristics of the training deformations in a statistical fashion. Simulated deformations can then be constructed by randomly sampling the resultant statistical distribution in an unconstrained or a landmark-constrained fashion. The paper also describes a model for generating tissue atrophy or growth in order to simulate intra-individual brain deformations. Several sets of simulated deformation fields and respective images are generated, which can be used in the future for systematic and extensive validation studies of automated atlas-based segmentation and deformable registration methods. The code and simulated data are available through our Web site.

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PubMed2006

Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping.

This paper proposes a 3D statistical model aiming at effectively capturing statistics of high-dimensional deformation fields and then uses this prior knowledge to constrain 3D image warping. The conventional statistical shape model methods, such as the active shape model (ASM), have been very successful in modeling shape variability. However, their accuracy and effectiveness typically drop dramatically in high-dimensionality problems involving relatively small training datasets, which is customary in 3D and 4D medical imaging applications. The proposed statistical model of deformation (SMD) uses wavelet-based decompositions coupled with PCA in each wavelet band, in order to more accurately estimate the pdf of high-dimensional deformation fields, when a relatively small number of training samples are available. SMD is further used as statistical prior to regularize the deformation field in an SMD-constrained deformable registration framework. As a result, more robust registration results are obtained relative to using generic smoothness constraints on deformation fields, such as Laplacian-based regularization. In experiments, we first illustrate the performance of SMD in representing the variability of deformation fields and then evaluate the performance of the SMD-constrained registration, via comparing a hierarchical volumetric image registration algorithm, HAMMER, with its SMD-constrained version, referred to as SMD+HAMMER. This SMD-constrained deformable registration framework can potentially incorporate various registration algorithms to improve robustness and stability via statistical shape constraints.

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PubMed2006

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation.

An approach to the deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the sought deformation map from the atlas to the image of a tumor patient are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences in brain shape, and the other representing tumor-induced deformation. For a new tumor case, a partial observation of the sought deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas image in order to generate an image that is similar to tumor patient's image, thereby facilitating the atlas registration process. Results for a real tumor case and a number of simulated tumor cases indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

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PubMed2006

Puberty-related influences on brain development.

Puberty is a time of striking changes in cognition and behavior. To indirectly assess the effects of puberty-related influences on the underlying neuroanatomy of these behavioral changes we will review and synthesize neuroimaging data from typically developing children and adolescents and from those with anomalous hormone or sex chromosome profiles. The trajectories (size by age) of brain morphometry differ between boys and girls, with girls generally reaching peak gray matter thickness 1-2 years earlier than boys. Both boys and girls with congenital adrenal hyperplasia (characterized by high levels of intrauterine testosterone), have smaller amygdala volume but the brain morphometry of girls with CAH did not otherwise significantly differ from controls. Subjects with XXY have gray matter reductions in the insula, temporal gyri, amygdala, hippocampus, and cingulate-areas consistent with the language-based learning difficulties common in this group.

Adolescent↗

PubMed2006

Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method.

This paper presents a novel deformable model for automatic segmentation of prostates from three-dimensional ultrasound images, by statistical matching of both shape and texture. A set of Gabor-support vector machines (G-SVMs) are positioned on different patches of the model surface, and trained to adaptively capture texture priors of ultrasound images for differentiation of prostate and nonprostate tissues in different zones around prostate boundary. Each G-SVM consists of a Gabor filter bank for extraction of rotation-invariant texture features and a kernel support vector machine for robust differentiation of textures. In the deformable segmentation procedure, these pretrained G-SVMs are used to tentatively label voxels around the surface of deformable model as prostate or nonprostate tissues by a statistical texture matching. Subsequently, the surface of deformable model is driven to the boundary between the tentatively labeled prostate and non-prostate tissues. Since the step of tissue labeling and the step of label-based surface deformation are dependent on each other, these two steps are repeated until they converge. Experimental results by using both synthesized and real data show the good performance of the proposed model in segmenting prostates from ultrasound images.

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PubMed2006

Learning-based deformable registration of MR brain images.

This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence detection during the registration procedure. This is achieved by optimizing an energy function that requires each point to have its best-scale geometric features consistent over the corresponding points in the training samples, and at the same time distinctive from those of nearby points in the neighborhood. Second, the active points used to drive the brain registration are hierarchically selected during the registration procedure, based on their saliency and consistency measures. That is, the image points with salient and consistent features (across different individuals) are considered for the initial registration of two images, while other less salient and consistent points join the registration procedure later. By incorporating these two novel strategies into the framework of the HAMMER registration algorithm, the registration accuracy has been improved according to the results on simulated brain data, and also visible improvement is observed particularly in the cortical regions of real brain data.

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PubMed2006

Hippocampus volume loss due to chronic heavy drinking.

OBJECTIVE: No clear consensus exists regarding the effect of sustained, heavy drinking on hippocampal volume. Our prior work hypothesized significantly lowered total hippocampus volumes in heavy chronically drinking alcohol-dependent (AD) subjects compared with light-drinking nondependent control subjects matched for age and gender. METHOD: Using a series of applicable exclusion criteria culled from previous published studies, we measured hippocampal volumes from MRI scan data acquired on a 3T scanner and subjected those data to automated volume analysis blind to the drinking history. RESULTS: Comparison with AD test (n=8) and non-AD control (n=8) subjects found significant lessening in total (p=0.020) and left (p=0.010) hippocampal volumes with a near-significant difference on the right (p=0.051). Linear regression demonstrated that neither total brain volume nor intracranial volume affected the hippocampus measures. CONCLUSIONS: These data support the view that heavy drinking exerts a unique and selectively injurious effect on the hippocampus. Further study in larger samples must verify this in a search for possible mechanisms of injury.

Adult↗

PubMed2006

Hypercortisolism in alcohol dependence and its relation to hippocampal volume loss.

OBJECTIVE: The effects of hypercortisolism on hippocampal volume have not been studied in heavy drinkers. Prior work suggested increased hypothalamic-pituitary-adrenal activity in relation to lowered total hippocampus volume (THV) in heavy-drinking alcohol-dependent (AD) subjects. The present study hypothesized the following: (1) that chronic heavy-drinking subjects would demonstrate significantly higher salivary cortisol concentrations than light-drinking control subjects and (2) that data from the whole sample group would present an inverse relationship between cortisol concentration and THV. METHOD: In carefully selected test and control subject groups matched for age, gender, and ethnicity, we measured salivary cortisol samples at waking, waking + 30 minutes, noon, and 4 PM on the day of magnetic resonance imaging of the brain. We next compared mean cortisol concentrations between groups and assessed the statistical association between cortisol concentration and hippocampus volume measures. RESULTS: Comparison of AD test subjects (n = 8) and non-AD control subjects (n = 8) found significantly higher cortisol concentrations at both morning sampling times (mean [SD] at waking: 0.49 [0.23] vs 0.24 [0.14] microg/dl, p = .012; at waking + 30 minutes: 0.57 [0.37] vs 0.28 [0.11] microg/dl, p = 0.043). Controlling for intracranial volume, there was a significant inverse correlation between waking cortisol concentration and THV (p = .007) in the total sample group (N = 16). However, when analyzed separately, only the control group maintained a strong, inverse association (p = .025). There was no association among the heavy drinking subjects. CONCLUSIONS: These early data in a small sample support the view that chronic heavy drinking results in high salivary cortisol concentrations. What remains unclear is whether hypercortisolism exerts a selectively injurious effect that results in observed hippocampus volume loss. Further research in larger groups using more frequent, monitored sampling must address the following: (1) whether this finding can be replicated and (2) if replicated, whether the lack of an association between low hippocampal volumes and high cortisol levels may indicate an extent of injury beyond which a normal association of the two may be lost.

Adult↗

PubMed2005

CLASSIC: consistent longitudinal alignment and segmentation for serial image computing.

This paper proposes a temporally consistent and spatially adaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorporates image-adaptive clustering, spatiotemporal smoothness constraints, and image warping to jointly segment a series of 3-D MR brain images of the same subject that might be undergoing changes due to development, aging, or disease. Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency.

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PubMed2005

Spatiotemporal maturation patterns of murine brain quantified by diffusion tensor MRI and deformation-based morphometry.

Highly heterogeneous spatiotemporal patterns of maturation of the murine brain during the first 80 postnatal days were examined by high-dimensional deformation-based morphometry applied to high-resolution diffusion tensor MRIs. The maturation profile revealed a sharp contrast between tissue anisotropy changes in the cortex and in major white-matter fibers. Radially oriented tissue anisotropy was measured during the first postnatal week in cortical regions, reflecting the underlying columnar organization of the cortex. Subsequently, tissue anisotropy reduced rapidly, potentially reflecting the growth of randomly oriented dendritic trees that reduce tissue coorientation. Distinct anisotropy patterns were also observed along layer I of the cortex and were attributed to thin fibers oriented parallel to the outer surface. Last, spatially complex patterns of maturation were measured in all major axonal pathways and in the hippocampus, caudate putamen, and cerebellum. This analysis provides a framework for quantifying normative maturation patterns against which phenotypes of mice of different genetic and environmental backgrounds can be contrasted.

Age Factors↗

PubMed2005

Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities.

CONTEXT: Neuroanatomic abnormalities in schizophrenia may underlie behavioral manifestations. Characterization of such abnormalities is required for interpreting functional data. Frontotemporal abnormalities have been documented by using predetermined region-of-interest approaches, but deformation-based morphometry permits examination of the entire brain. OBJECTIVES: To perform whole-brain analyses of structural differences between patients with schizophrenia and controls, to examine sex and medication effects, and to apply a high-dimensional nonlinear pattern classification technique to quantify the degree of separation between patients and controls, thereby testing the potential of this new technique as an aid to diagnostic procedures. DESIGN: Whole-brain morphologic analysis using high-dimensional shape transformations. SETTING: Schizophrenia Research Center, University of Pennsylvania Medical Center. PARTICIPANTS: Neuroleptic-naïve and previously treated patients with DSM-IV schizophrenia (n = 69) and sociodemographically matched controls (n = 79). MAIN OUTCOME MEASURES: Gray matter, white matter, and ventricular cerebrospinal fluid volumes in the brain. RESULTS: Magnetic resonance images showed reduced gray matter and increased ventricular cerebrospinal fluid volumes in patients with schizophrenia in the whole brain and in specific foci: the hippocampus and adjacent white matter, the cingulate and orbitofrontal cortex, the frontotemporal and parietotemporal areas, and the occipital areas near the lingual gyrus. The classifier had a mean classification accuracy of 81.1% for men and women combined (82% for women and 85% for men, when each group was treated separately), determined via cross-validation. CONCLUSIONS: This study confirms previous findings of reduced frontotemporal volumes and suggests new hypotheses, especially involving occipital association and speech production areas. It also suggests finer localization of volume reduction in the hippocampus and other limbic structures and in the frontal lobe. Pattern classification showed high sensitivity and specificity for the diagnosis of schizophrenia, suggesting the potential utility of magnetic resonance imaging as a diagnostic aid.

Adult↗

PubMed2005

Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM.

This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using high-dimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size.

Algorithms↗

PubMed2005

Consistent estimation of cardiac motions by 4D image registration.

A 4D image registration method is proposed for consistent estimation of cardiac motion from MR image sequences. Under this 4D registration framework, all 3D cardiac images taken at different time-points are registered simultaneously, and motion estimated is enforced to be spatiotemporally smooth, thereby overcoming potential limitations of some methods that typically estimate cardiac deformation sequentially from one frame to another, instead of treating the entire set of images as a 4D volume. To facilitate our image matching process, an attribute vector is designed for each point in the image to include intensity, boundary and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points for refinement of registration. Experimental results on real data demonstrate good performance of the proposed method in registering cardiac images and estimating motions from cardiac image sequences.

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PubMed2005

Learning best features for deformable registration of MR brains.

This paper presents a learning method to select best geometric features for deformable brain registration. Best geometric features are selected for each brain location, and used to reduce the ambiguity in image matching during the deformable registration. Best geometric features are obtained by solving an energy minimization problem that requires the features of corresponding points in the training samples to be similar, and the features of a point to be different from those of nearby points. By incorporating those learned best features into the framework of HAMMER registration algorithm, we achieved about 10% improvement of accuracy in estimating the simulated deformation fields, compared to that obtained by HAMMER. Also, on real MR brain images, we found visible improvement of registration in cortical regions.

Algorithms↗

PubMed2005

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation.

An approach to deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the desired deformation map are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences, and the other involving tumor-induced deformation. For a new tumor case, a partial observation of the desired deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas to generate an image that is more similar to brain tumor image, thereby facilitating the atlas registration process. Results for a real and a simulated tumor case indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

Algorithms↗

PubMed2005

Statistical representation and simulation of high-dimensional deformations: application to synthesizing brain deformations.

This paper proposes an approach to effectively representing the statistics of high-dimensional deformations, when relatively few training samples are available, and conventional methods, like PCA, fail due to insufficient training. Based on previous work on scale-space decomposition of deformation fields, herein we represent the space of "valid deformations" as the intersection of three subspaces: one that satisfies constraints on deformations themselves, one that satisfies constraints on Jacobian determinants of deformations, and one that represents smooth deformations via a Markov Random Field (MRF). The first two are extensions of PCA-based statistical shape models. They are based on a wavelet packet basis decomposition that allows for more accurate estimation of the covariance structure of deformation or Jacobian fields, and they are used jointly due to their complementary strengths and limitations. The third is a nested MRF regularization aiming at eliminating potential discontinuities introduced by assumptions in the statistical models. A randomly sampled deformation field is projected onto the space of valid deformations via iterative projections on each of these subspaces until convergence, i.e. all three constraints are met. A deformation field simulator uses this process to generate random samples of deformation fields that are not only realistic but also representative of the full range of anatomical variability. These simulated deformations can be used for validation of deformable registration methods. Other potential uses of this approach include representation of shape priors in statistical shape models as well as various estimation and hypothesis testing paradigms in the general fields of computational anatomy and pattern recognition.

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PubMed2004

Optimized prostate biopsy via a statistical atlas of cancer spatial distribution.

A methodology is presented for constructing a statistical atlas of spatial distribution of prostate cancer from a large patient cohort, and it is used for optimizing needle biopsy. An adaptive-focus deformable model is used for the spatial normalization and registration of 100 prostate histological samples, which were provided by the Center for Prostate Disease Research of the US Department of Defense, resulting in a statistical atlas of spatial distribution of prostate cancer. Based on this atlas, a statistical predictive model was developed to optimize the needle biopsy sites, by maximizing the probability of detecting cancer. Experimental results using cross-validation show that the proposed method can detect cancer with a 99% success rate using seven needles, in these samples.

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