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P4-591: THE DEVELOPMENT OF A HARMONIZED SEGMENTATION PROTOCOL FOR HIPPOCAMPAL SUBFIELDS: AN UPDATE

Alzheimer's & Dementia 2019年第7S_PART_30期15卷

作者： Laura Wisse David Berron Rosanna Olsen Ana M. Daugherty Katrin Amunts Jean Augustinack Arnold Bakker Andrew Bender Marina Boccardi Martina Bocchetta Mallar Chakravarty Gaelle Chetelat Robin De Flores Jordan DeKraker Song-Lin Ding Ricardo Insausti Olga Kedo Susanne G. Mueller Noa Ofen Daniela Palombo Naftali Raz Craig E. Stark Lei Wang Paul A. Yushkevich Qijing Yu Valerie A. Carr Renaud La Joie Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Clinical Memory Research Unit Lund University Lund Sweden University of Toronto Toronto ON Canada Beckman Institute for Advanced Science and Technology Champaign IL USA Institute of Neuroscience and Medicine (INM-1) Jülich Germany Massachusetts General Hospital Charlestown MA USA Johns Hopkins School of Medicine Baltimore MD USA Michigan State University East Lansing MI USA IRCCS S Giovanni di Dio Fatebenefratelli Brescia Italy Dementia Research Centre Queen Square Institute of Neurology University College London London United Kingdom McGill University Montreal QC Canada Inserm UMR-S U1237 Université de Caen-Normandie Caen France The University of Western Ontario London ON Canada Allen Brain Institute Seattle WA USA University of Castilla-La Mancha Albacete Spain Forschungszentrum Jülich Julich Germany Center for Imaging of Neurodegenerative Diseases San Francisco CA USA Wayne State University Detroit MI USA Boston University School of Medicine Boston MA USA University of California Irvine Irvine CA USA Northwestern University Chicago IL USA San Jose State University San Jose CA USA University of California San Francisco San Francisco CA USA

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ALZHEIMER’S DISEASE AND THE HIPPOCAMPUS: NOVEL INSIGHTS FROM AN EX VIVO COMPUTATIONAL ATLAS COMBINING MRI AND HISTOLOGY

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Alzheimer's & Dementia 2017年第7期13卷 P1534-P1535页

作者： Laura E.M. Wisse Daniel Adler Ranjit Ittyerah John B. Pluta Song-Lin Ding Long Xie Jiancong Wang Salmon Kadivar John L. Robinson Theresa Schuck John Q. Trojanowski Murray Grossman John A. Detre Mark A. Elliott Jon B. Toledo Weixia Liu Stephen Pickup Sandhitsu R. Das David A. Wolk Paul A. Yushkevich Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Allen Brain Institute Seattle WA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn FTD Center University of Pennsylvania Philadelphia PA USA Institute on Aging / University of Pennsylvania Philadelphia PA USA Houston Methodist Hospital Houston TX USA Penn Memory Center University of Pennsylvania Philadelphia PA USA

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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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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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Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI

Reconstruction of the human hippocampus in 3D from histology...

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IEEE International Symposium on Biomedical Imaging

作者： Daniel H. Adler Alex Yang Liu John Pluta Salmon Kadivar Sylvia Orozco Hongzhi Wang James C. Gee Brian B. Avants Paul A. Yushkevich Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA

In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We present an evaluation of reconstruction accuracy that is based on measures of similarity between boundaries drawn on both histology and MRI. Our methodology is currently being applied to an MRI atlas of the human hippocampal formation, in which atlas anatomical labels are derived from segmentation of reconstructed histology.

关键词： Magnetic resonance imaging image reconstruction Hippocampus Humans Stacking Hafnium Shape

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"Nonparametric Local Smoothing" is not image registration

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BMC Research Notes 2012年第1期5卷 1-5页

作者： Rohlfing, Torsten Avants, Brian Neuroscience Program SRI International Menlo Park CA 94025 333 Ravenswood Avenue United States Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania School of Medicine Philadelphia PA 19104 United States

Background: image registration is one of the most important and universally useful computational tasks in biomedical image analysis. A recent article by Xing & Qiu (IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2081-2092, 2011) is based on an inappropriately narrow conceptualization of the image registration problem as the task of making two images look alike, which disregards whether the established spatial correspondence is plausible. The authors propose a new algorithm, Nonparametric Local Smoothing (NLS) for image registration, but use image similarities alone as a measure of registration performance, although these measures do not relate reliably to the realism of the correspondence map. Results: Using data obtained from its authors, we show experimentally that the method proposed by Xing & Qiu is not an effective registration algorithm. While it optimizes image similarity, it does not compute accurate, interpretable transformations. Even judged by image similarity alone, the proposed method is consistently outperformed by a simple pixel permutation algorithm, which is known by design not to compute valid registrations. Conclusions: This study has demonstrated that the NLS algorithm proposed recently for image registration, and published in one of the most respected journals in computer science, is not, in fact, an effective registration method at all. Our results also emphasize the general need to apply registration evaluation criteria that are sensitive to whether correspondences are accurate and mappings between images are physically interpretable. These goals cannot be achieved by simply reporting image similarities. © 2012 Rohlfing and Avants;licensee BioMed Central Ltd.

关键词： Accuracy Correspondence image registration

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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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Agreement between the white matter connectivity based on the tensor-based morphometry and the volumetric white matter parcellations based on diffusion tensor imaging

Agreement between the white matter connectivity based on the...

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IEEE International Symposium on Biomedical Imaging

作者： Seung-Goo Kim Brian B. Avants Hyekyoung Lee James C. Gee Moo K. Chung Richard J Davidson Jamie L. Hanson Seth D. Pollak Department of Brain and Cognitive Sciences Seoul National University South Korea Department of Brain and Cognitive Sciences Department of Nuclear Medicine Institute of Radiation Medicine Medical Research Center Seoul National University South Korea Department of Biostatistics and Medical Informatics Waisman Laboratory for Brain Imaging and Behavior University of Wisconsin Madison WI USA Waisman Laboratory for Brain Imaging and Behavior Department of Psychology University of Wisconsin Madison WI USA Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Psychology University of Wisconsin Madison WI USA Institute of Radiation Medicine Medical Research Center Seoul National University South Korea

We are interested in investigating white matter connectivity using a novel computational framework that does not use diffusion tensor imaging (DTI) but only uses T1-weighted magnetic resonance imaging. The proposed method relies on correlating Jacobian determinants across different voxels based on the tensor-based morphometry (TBM) framework. In this paper, we show agreement between the TBM-based white matter connectivity and the DTI-based white matter atlas. As an application, altered white matter connectivity in a clinical population is determined.

关键词： Correlation Diffusion tensor imaging Biomedical imaging Educational institutions Brain

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Comparison of volumetric registration algorithms for tensor-based morphometry

Comparison of volumetric registration algorithms for tensor-...

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IEEE International Symposium on Biomedical Imaging

作者： Julio Villalon Anand A. Joshi Natasha Lepore Caroline Brun Arthur W. Toga Paul M. Thompson Laboratory of Neuro Imaging School of Medicine University of California Los Angeles Los Angeles CA USA Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Department of Radiology Childrens Hospital Los Angeles University of Southern California Los Angeles CA USA

ISBN: (纸本)9781424441273

Nonlinear registration of brain MRI scans is often used to quantify morphological differences associated with disease or genetic factors. Recently, surface-guided fully 3D volumetric registrations have been developed that combine intensity-guided volume registrations with cortical surface constraints. In this paper, we compare one such algorithm to two popular high-dimensional volumetric registration methods: large-deformation viscous fluid registration, formulated in a Riemannian framework, and the diffeomorphic “Demons” algorithm. We performed an objective morphometric comparison, by using a large MRI dataset from 340 young adult twin subjects to examine 3D patterns of correlations in anatomical volumes. Surface-constrained volume registration gave greater effect sizes for detecting morphometric associations near the cortex, while the other two approaches gave greater effects sizes subcortically. These findings suggest novel ways to combine the advantages of multiple methods in the future.

关键词： Correlation Fluids Three dimensional displays Volume measurement Genetics Surface morphology Brain

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Structural connectivity via the tensor-based morphometry

Structural connectivity via the tensor-based morphometry

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IEEE International Symposium on Biomedical Imaging

作者： Seung-Goo Kim Moo K. Chung Jamie L. Hanson Brian B. Avants James C. Gee Richard J. Davidson Seth D. Pollak Department of Brain and Cognitive Sciences Seoul National University South Korea Waisman Laboratory for Brain Imaging and Behavior University of Wisconsin Madison WI USA University of Wisconsin System Madison WI US Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781424441273

The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called the e-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children.

关键词： Correlation Diffusion tensor imaging Biomedical imaging Brain modeling Neuroscience Joining processes

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