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12th International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Zhang, Hui Yushkevich, Paul A. Rueckert, Daniel Gee, James C. Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania United States Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783642042706

Tensor-based morphometry (TBM) is a powerful approach for examining shape changes in anatomy both across populations and in time. Our work extends the standard TBM for quantifying local volumetric changes to establish both rich and intuitive descriptors of shape changes in fibrous structures. It leverages the data from diffusion tensor imaging to determine local spatial configuration of fibrous structures and combines this information with spatial transformations derived from image registration to quantify fibrous structure-specific changes, such as local changes in fiber length and in thickness of fiber bundles. In this paper, we describe the theoretical framework of our approach in detail and illustrate its application to study brain white matter. Our results show that additional insights can be gained with the proposed analysis. © 2009 Springer-Verlag.

关键词： Diffusion tensor imaging

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White matter hyperintensities are more highly associated with preclinical Alzheimer's disease than imaging and cognitive markers of neurodegeneration

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Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring 2016年 4卷 18-27页

作者： Kandel, Benjamin M. Avants, Brian B. Gee, James C. McMillan, Corey T. Erus, Guray Doshi, Jimit Davatzikos, Christos Wolk, David A. Penn Image Computing and Science Laboratory and Department of Bioengineering University of Pennsylvania Philadelphia PA United States Penn Image Computing and Science Laboratory and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Neurology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States

Introduction: Cognitive tests and nonamyloid imaging biomarkers do not consistently identify preclinical AD. The objective of this study was to evaluate whether white matter hyperintensity (WMH) volume, a cerebrovascular disease marker, is more associated with preclinical AD than conventional AD biomarkers and cognitive tests. Methods: Elderly controls enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 158) underwent florbetapir-PET scans, psychometric testing, neuroimaging with MRI and PET, and APOE genetic testing. Elderly controls the Parkinson's progression markers initiative (PPMI, n = 58) had WMH volume, cerebrospinal fluid (CSF) Aβ_1-42, and APOE status measured. Results: In the ADNI cohort, only WMH volume and APOE ε4 status were associated with cerebral Aβ (standardized β = 0.44 and 1.25, P = .03 and .002). The association between WMH volume and APOE ε4 status with cerebral Aβ (standardized β = 1.12 and 0.26, P = .048 and .045) was confirmed in the PPMI cohort. Discussion: WMH volume is more highly associated with preclinical AD than other AD biomarkers. © 2016 The Authors.

关键词： Aging Alzheimer's disease Amyloid Leukoaraiosis MRI PET Preclinical Alzheimer's disease White matter

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Deep Learning in Medical image Registration: Magic or Mirage? 38

Deep Learning in Medical Image Registration: Magic or Mirage...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jena, Rohit Sethi, Deeksha Chaudhari, Pratik Gee, James C. Computer and Information Science United States Electrical and Systems Engineering United States Radiology United States Penn Image Computing and Science Laboratory United States

Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. We reassess and recalibrate performance expectations from classical and DLIR methods under access to label supervision, training time, and its generalization capabilities under minor domain shifts. © 2024 Neural information processing systems foundation. All rights reserved.

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Dependency prior for multi-atlas label fusion

Dependency prior for multi-atlas label fusion

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IEEE International Symposium on Biomedical Imaging

作者： Hongzhi Wang Paul A Yushkevich Penn Image Computing and Science Laboratory University of Pennsylvania USA

Multi-atlas label fusion has been widely applied in medical image analysis. To reduce the bias in label fusion, we proposed a joint label fusion technique to reduce correlated errors produced by different atlases via considering the pair-wise dependencies between them. Using image similarities from image patches to estimate the pairwise dependencies, we showed promising performance. To address the unreliability in purely using local image similarity for dependency estimation, we propose to improve the accuracy of the estimated dependencies by including empirical knowledge, which is learned from the atlases in a leave-one-out strategy. We apply the new technique to segment the hippocampus from MRI and show significant improvement over our initial results.

关键词： image segmentation Estimation Hippocampus Joints Training Reliability Accuracy

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Multi-start Method with Prior Learning for image Registration

Multi-start Method with Prior Learning for Image Registratio...

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International Conference on Computer Vision (ICCV)

作者： Gang Song Brian B. Avants James C. Gee Penn Image Computing and Science Laboratory University of Pennsylvania USA

We propose an efficient image registration strategy that is based on learned prior distributions of transformation parameters. These priors are used to constrain a finite- time multi-start optimization method. Motivation for this approach comes from the fact that standard affine brain image registration methods, especially those based on gradient descent optimization alone, are affected by the initial search position. While global optimization methods can resolve this problem, they are are often very time consuming. Our goal is to build an explicit prior model of the gap between a typical registration solution and the solution gained by a global optimization method. We use this learned prior model to restrict randomized search in the relevant parameter space surrounding the initial solution. Global optimization in this restricted parameter space provides, in finite time, results that are superior to both gradient descent and the general multi-start strategy. The performance of our method is illustrated on a data set of 67 elderly and neurodegenerative brains. Our novel learning strategy and the associated registration method are shown to outperform other approaches. Theoretical, synthetic and real-world examples illustrate this improvement.

关键词： image registration Cost function Optimization methods image analysis Laboratories Senior citizens Design methodology image sampling Tellurium Humans

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Spatial bias in multi-atlas based segmentation

Spatial bias in multi-atlas based segmentation

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hongzhi Wang Paul A. Yushkevich Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania USA

Multi-atlas segmentation has been widely applied in medical image analysis. With deformable registration, this technique realizes label transfer from pre-labeled atlases to unknown images. When deformable registration produces error, label fusion that combines results produced by multiple atlases is an effective way for reducing segmentation errors. Among the existing label fusion strategies, similarity-weighted voting strategies with spatially varying weight distributions have been particularly successful. We show that, weighted voting based label fusion produces a spatial bias that under-segments structures with convex shapes. The bias can be approximated as applying spatial convolution to the ground truth spatial label probability maps, where the convolution kernel combines the distribution of residual registration errors and the function producing similarity-based voting weights. To reduce this bias, we apply a standard spatial deconvolution to the spatial probability maps obtained from weighted voting. In a brain image segmentation experiment, we demonstrate the spatial bias and show that our technique substantially reduces this spatial bias.

关键词： image segmentation Kernel Convolution Deconvolution Hippocampus Accuracy Magnetic resonance imaging

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Estimation of image Bias Field with Sparsity Constraints

Estimation of Image Bias Field with Sparsity Constraints

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Yuanjie Zheng James C. Gee Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania School of Medicine

ISBN: (纸本)9781424469840;9781424469857

We propose a new scheme to estimate image bias field through introducing two sparsity constraints. One is that the bias-free image has concise representation with image gradients or coefficients of other image transformations. The other constraint is that model fit on the bias field should be as concise as possible. The new scheme enables adaptive specifications of the estimated bias field's smoothness, and results in extremely accurate solutions with more efficient optimization techniques, e.g. linear programming. These distinguish our approaches from many previous methods. Our techniques can be applied to intensity inhomogeneity correction of medical images, illumination and vignetting estimation of images captured by digital cameras.

关键词： images imagery (Psychotherapy) image Bias Bias Bias Statistical Biases bias(electronics) Digital cameras mode matching graph transformation Constraint VIGNETTING

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Nonparametric Markov priors for tissue segmentation

Nonparametric Markov priors for tissue segmentation

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IEEE International Symposium on Biomedical Imaging

作者： Zhuang Song Suyash P. Awate James C. Gee Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781424420025

This paper presents a novel method to construct a probabilistic tissue prior, for Bayesian tissue segmentation, which is based on nonparametric Markov statistics of tissue intensities learned from training data. The proposed nonparametric Markov (NPM) prior is in contrast to the conventional tissue- probability-map (TPM) prior that is based on the voxel location in a common anatomical template space. Given a set of manually labeled voxels as the training set, the NPM prior is constructed by learning a fuzzy classification function that distinguishes the Markov statistics of tissue intensities in a statistical supervised-learning framework. The validation experiments in this paper compare the efficacy of the NPM prior to that of the TPM prior in producing tissue segmentations, and demonstrate the advantages of the NPM prior, qualitatively and quantitatively, over the TPM prior, especially in cortical regions.

关键词： image segmentation Statistics Probability Biomedical imaging Bayesian methods Biological tissues Training data Learning systems Magnetic resonance imaging image analysis

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Adaptive graph cuts with tissue priors for brain MRI segmentation

Adaptive graph cuts with tissue priors for brain MRI segment...

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IEEE International Symposium on Biomedical Imaging

作者： Zhuang Song N. Tustison B. Avants J. Gee Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)078039576X

We describe a novel framework for automatic brain MRI tissue segmentation. To overcome inherent difficulties associated with this particular segmentation problem, we use a graph cut/atlas-based registration methodology optimized within an iterative mode. The basic graph cut algorithm guarantees a global or near-global minimum of an energy function associated with a Markov random field (MRF). For atlas-based graph cuts, we tailor this energy function to incorporate both a priori information derived from registered brain atlases as well as region and boundary information derived directly from the images. The iterative algorithm adaptively alternates segmentation and inhomogeneity correction. The proposed method can be extended to multispectral image segmentation. We validate our method in both simulated adult and real neonatal brain MR images corrupted by significant noise and intensity inhomogeneities

关键词： Magnetic resonance imaging image segmentation Brain modeling Iterative algorithms Markov random fields Acoustic noise Optimization methods Partitioning algorithms Multispectral imaging Pediatrics

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Hippocampus segmentation using a stable maximum likelihood classifier ensemble algorithm

Hippocampus segmentation using a stable maximum likelihood c...

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IEEE International Symposium on Biomedical Imaging

作者： Hongzhi Wang Jung Wook Suh Sandhitsu Das Murat Altinay John Pluta Paul Yushkevich Penn Image Computing and Science Laboratory Departments of Radiology University of Pennsylvania USA

ISBN: (纸本)9781424441273

We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids the numerical instability problem. Experiments on a hippocampus segmentation problem using the ADNI data show that our algorithm consistently converges faster and generalizes better than AdaBoost.

关键词： image segmentation Training Hippocampus Error correction Additives Joints Testing

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