% This file was created with JabRef 2.3.1. % Encoding: Cp1252 @INPROCEEDINGS{barnes08caph, author = {Josephine Barnes and Sebastien Ourselin and Nick C. Fox}, title = {Clinical application of measurement of hippocampal atrophy in degenerative diseases}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {129--140}, month = {September}, abstract = {Hippocampal atrophy is a characteristic and early feature of Alzheimer’s disease. Volumetry of the hippocampus using T1-weighted magnetic resonance imaging (MRI) has been used to assess hippocampal involvement in different neurodegenerative diseases, to understand the natural history of disease, and also to track changes in volume over time. Assessing change in volume circumvents issues surrounding inter-individual variability and allows assessment of disease progression. Disease-modifying effects of putative therapies are important to assess in clinical trials and are difficult using clinical scales. As a result there is increasing use of serial MRI in trials to detect a slowing of atrophy to give evidence of slowing of progression. Automated and yet reliable methods of quantifying such change in the hippocampus would therefore be very valuable. As a result, we have developed and assessed algorithms capable of measuring such changes automatically. Such methods may also be applicable to clinical situations such as predicting those people who may decline to a diagnosis of dementia in the future. This paper describes the work in understanding the changes in the hippocampal region in differing diseases and attempts to improve the accuracy and precision of automated hippocampal segmentation in clinical datasets.}, url = {http://picsl.upenn.edu/caph08/papers/paper06.pdf} } @INPROCEEDINGS{chupin08caph, author = {Marie Chupin and R\'emi Cuingnet and Louis Lemieux and St\'ephane Leh\'ericy and Habib Benali and Line Garnero and Olivier Colliot and {the Alzheimer's Disease Neuroimaging Initiative}}, title = {Fully Automatic Hippocampus Segmentation Discriminates between {A}lzheimer's Disease and Normal Aging - Data from the {ADNI} database}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {35--45}, month = {September}, abstract = {The hippocampus is among the first structures affected in Alzheimer's disease (AD); hippocampal MRI volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We propose a fully automatic method using probabilistic and anatomical priors for hippocampus segmentation. Probabilistic information is derived from 16 young controls and anatomical knowledge is modeled with automatically detected landmarks. The results were previously evaluated by comparison with manual segmentation on data from 16 young healthy controls, with a leave-one-out strategy, and 8 AD patients. High accuracy was found for both groups (volume error 6\% and 7\%, overlap 87\% and 86\%, respectively). The method was used here to segment 29 patients with AD and 30 elderly normal subjects chosen at random from the ADNI (Alzheimer's Disease Neuroimaging Initiative) database. The segmentation proved qualitatively acceptable in all 59 cases except 4 cases. The classification proved accurate, with 82\% of the AD patients correctly classified with respect to the elderly controls, when only the hippocampal volume was taken into account.}, url = {http://picsl.upenn.edu/caph08/papers/paper08.pdf} } @INPROCEEDINGS{das08caph, author = {Sandhitsu R. Das and Dawn Mechanic-Hamilton and Marc Korczykowski and John Pluta and Simon Glynn and Brian B. Avants and John A. Detre and James C. Gee and Paul A. Yushkevich}, title = {Spatial Correspondence Based Asymmetry Analysis in Hippocampus: Application to Temporal Lobe Epilepsy}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {13--21}, month = {September}, abstract = {Quantification of functional and structural asymmetry in the brain can provide clinically useful information. In the study of temporal lobe epilepsy (TLE), such analyses are often carried out within the hippocampus. Functional asymmetry is typically expressed in terms of differences in the number of suprathreshold voxels activated, normalized to total activation, within the structure of interest, while the subjects perform a cognitive task in a functional magnetic resonance imaging (fMRI) experiment. Structural asymmetry is usually expressed in terms of normalized, relative hippocampal volume differences between hemispheres. We introduce methodologies for carrying out asymmetry analysis for region of interest (ROI) based studies that take into account information about spatial correspondence of voxels on two sides of the brain. We apply this methodology to make determination of hemispheric specialization during a memory encoding task in patients with refractory TLE. Memory lateralization is an important step in the presurgical evaluation of such patients for temporal lobectomy. Our functional asymmetry scores in hippocampus are found to have a strong correspondence with hemispheric dominance given by Intracarotid Amobarbital Testing (IAT), which is the widely accepted {\em gold standard} for determining laterality. We also use local thickness measurements to study structural asymmetry within hippocampus. Regional variation in thickness differences between different subgroups are revealed using the correspondence based approach.}, owner = {pauly}, timestamp = {2008.09.10}, url = {http://picsl.upenn.edu/caph08/papers/paper09.pdf} } @INPROCEEDINGS{gutman08caph, author = {Boris Gutman and Yalin Wang and Jonathan Morra and Arthur Toga and Paul Thompson}, title = {Disease Classification with Hippocampal Shape Invariants}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {76--86}, month = {September}, abstract = {We present the first Support Vector Machine classification study using the feature space of shape invariants of hippocampal surfaces. Our shape invariants are based on rotationally invariant properties of spherical harmonics (SPH). A global conformal map is used for parameterization. Leave-one-out testing on 49 Alzheimer(AD) and 63 elderly control subjects yielded 75.5\% sensitivity and 87.3\% specificity with 82.1\% correct overall.}, url = {http://picsl.upenn.edu/caph08/papers/paper12.pdf} } @INPROCEEDINGS{lefaucheur08caph, author = {Le Faucheur, Xavier and Brani Vidakovic and Delphine Nain and Allen Tannenbaum}, title = {Adaptive Bayesian Shrinkage Model for Spherical Wavelet Based Denoising and Compression of Hippocampus Shapes}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {87--96}, month = {September}, abstract = {This paper presents a novel wavelet-based denoising and compression statistical model for 3D hippocampus shapes. Shapes are encoded using spherical wavelets and the objective is to remove noisy coefficients while keeping significant shape information. To do so, we develop a non-linear wavelet shrinkage model based on a data-driven Bayesian framework. We threshold wavelet coefficients by locally taking into account shape curvature and interscale dependencies between neighboring wavelet coefficients. Our validation shows how this new wavelet shrinkage framework outperforms classical compression and denoising methods for shape representation. We apply our method to the denoising of the left hippocampus from MRI brain data.}, url = {http://picsl.upenn.edu/caph08/papers/paper11.pdf} } @INPROCEEDINGS{morra08caph, author = {Jonathan H. Morra and Zhuowen Tu and Liana G. Apostolova and Amity E. Green and Arthur W. Toga and Paul M. Thompson}, title = {Automatic Subcortical Segmentation Using a Contextual Model}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {97--104}, month = {September}, abstract = {Automatically segmenting subcortical structures in brain images has the potential to greatly accelerate drug trials and population studies of disease. Here we propose an automatic subcortical segmentation algorithm using the auto context model (ACM). Unlike many segmentation algorithms that separately compute a shape prior and an image appearance model, we develop a framework based on machine learning to learn a unified appearance and context model. We trained our algorithm to segment the hippocampus and tested it on 83 brain MRIs (of 35 Alzheimer's disease patients, 22 with mild cognitive impairment, and 26 normal healthy controls). Using standard distance and overlap metrics, the auto context model method significantly outperformed simpler learning-based algorithms (using AdaBoost alone) and the FreeSurfer system.}, url = {http://picsl.upenn.edu/caph08/papers/paper02.pdf} } @INPROCEEDINGS{mueller08caph, author = {Susanne Mueller}, title = {The Hippocampus: Why Regional Information Matters}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {1}, month = {September}, note = {Invited presentation}, abstract = {The aim of this lecture is to give the audience a short overview of the function and anatomy of the hippocampus, a structure which has always exuded a special fascination on neuroscientists and neurologists. The hippocampus is a peculiarly shaped structure in the medial temporal lobe which plays a crucial role in memory processes and learning. Its special electrophysiological and neurochemical properties render it particularly susceptible to insults. As a consequence, the hippocampus is affected by a variety of neurodegenerative diseases, e.g.. Alzheimer’s disease, epilepsy, major depression, post-traumatic stress or multiple sclerosis etc. Neuroimaging studies traditionally assess function and structure of the hippocampus as a whole. However, the hippocampus is not a homogeneous structure but consists of several histologically and functionally distinct hippocampal subfields; subiculum, the cornu ammonis sectors (CA) and the dentate gyrus. Histopathological studies have shown that different disease processes affect those subfields differently. Therefore, there has been quite some interest in developing neuroimaging acquisition and post-processing techniques which allow to obtain functional and structural information about hippocampal subfields rather than just about the hippocampus as a whole. The lecture will provide some examples how structural measurements of hippocampal subfields using high resolution MR images provide superior information compared to measurement of the total hippocampal volume for early detection and differentiation of different neurodegenerative processes.} } @INPROCEEDINGS{pluta08caph, author = {John Pluta and Brian B. Avants and Simon Glynn and Suyash Awate and James C. Gee and John A. Detre}, title = {Appearance and Incomplete Label Matching for Diffeomorphic Template Based Hippocampus Segmentation}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {105--116}, month = {September}, abstract = {We present a robust, high-throughput, semi-automated template based protocol for segmenting the hippocampus in temporal lobe epilepsy (TLE). The semi-automated component of this approach, which minimizes user effort while maximizing the benefit of human input to the algorithm, relies on "incomplete labeling." Incomplete labeling requires the user to quickly and approximately segment a few key regions of the hippocampus through a user-interface. Subsequently, this partial labeling of the hippocampus is combined with image similarity terms to guide volumetric diffeomorphic normalization between an individual brain and an unbiased disease-specific template, with fully labeled hippocampi. We solve this many-to-few and few-to-many matching problem, and gain robustness to inter and intra-rater variability and small errors in user labeling, by embedding the template-based normalization within a probabilistic framework that examines both label geometry and appearance data at each label. We evaluate the reliability of this framework with respect to manual labeling and show that it increases minimum performance levels relative to fully automated approaches and provides high inter-rater reliability. Thus, this approach does not require expert neuroanatomical training and is viable for high-throughput studies of both the normal and the highly atrophic hippocampus.}, url = {http://picsl.upenn.edu/caph08/papers/paper14.pdf} } @INPROCEEDINGS{shen08caph, author = {Li Shen and Hiram A. Firpi and Andrew J. Saykin and John D. West}, title = {Parametric Surface Modeling and Registration for Comparison of Manual and Automated Segmentation of the Hippocampus}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {117--128}, month = {September}, abstract = {Accurate and efficient segmentation of the hippocampus from brain images is a challenging issue. Although experienced anatomic tracers can be reliable, manual segmentation is a time consuming process and may not be feasible for large-scale neuroimaging studies. In this paper, we compare an automated method, FreeSurfer (V4), with a published manual protocol on the determination of hippocampal boundaries from MRI scans, using data from an existing MCI/AD cohort. To perform the comparison, we develop an enhanced spherical harmonic processing framework to model and register these hippocampal traces. The framework treats the two hippocampi as a single geometric configuration and extracts the positional, orientation and shape variables in a multi-object setting. We apply this framework to register manual tracing and FreeSurfer results together and the two methods show stronger agreement on position and orientation than shape measures. Work is in progress to examine a refined FreeSurfer segmentation strategy and an improved agreement on shape features is expected.}, url = {http://picsl.upenn.edu/caph08/papers/paper07.pdf} } @INPROCEEDINGS{vanleemput08caph, author = {Van Leemput, Koen and Akram Bakkour and Thomas Benner and Graham Wiggins and Lawrence L. Wald and Jean Augustinack and Bradford C. Dickerson and Polina Golland and Bruce Fischl}, title = {Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo {MRI}}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {46--55}, month = {September}, abstract = {Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. In this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampal area are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals.}, url = {http://picsl.upenn.edu/caph08/papers/paper04.pdf} } @INPROCEEDINGS{wang08caph, author = {Lei Wang and Ali Khan and John G. Csernansky and Bruce Fischl and Michael I. Miller and John C. Morris and M. Faisal Beg}, title = {Fully-Automated, Multi-Stage Hippocampus Mapping in Very Mild {A}lzheimer Disease}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {22--34}, month = {September}, abstract = {Landmark-based high-dimensional diffeomorphic maps of the hippocampus, while accurate, is highly-depended on rater's anatomic knowledge of the hippocampus in the magnetic resonance images. It is therefore vulnerable to rater drift and errors if substantial amount of effort is not spent on quality assurance, training and re-training. A fully-automated, FreeSurfer-initialized large-deformation diffeomorphic metric mapping procedure of small brain sub-structures, including the hippocampus, has been previously developed and validated in small samples. In this report, we demonstrate that this fully-automated pipeline can be used in place of the landmark-based procedure in a large-sample clinical study to produce similar statistical outcomes. Some direct comparisons of the two procedures are also presented.}, url = {http://picsl.upenn.edu/caph08/papers/paper10.pdf} } @INPROCEEDINGS{xie08caph, author = {Jing Xie and Dan Alcantara and Nina Amenta and Evan Fletcher and Oliver Martinez and Maria Persianinova and Charles DeCarli and Owen Carmichael}, title = {Spatially-Localized Hippocampal Shape Analysis in Late-Life Cognitive Decline}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {2--12}, month = {September}, abstract = {We present a method for generating data-driven, concise, and spatially-localized parameterizations of hippocampal (HP) shape, and use the method to analyze HP atrophy in late-life cognitive decline. The method optimizes a set of shape basis vectors (shape components) that strike a balance between spatial locality and compact representation of population shape characteristics. The method can be used for exploratory analysis of localized shape deformations in any population of HP on which point-to-point correspondence mappings have been established via anatomical landmarking or high-dimensional warping. Experiments combine the method with an automated HP to HP mapping method to analyze tracings of 101 elderly subjects with normal cognition, mild cognitive impairment (MCI), and Alzheimer's Disease (AD) from an AD Center population. Results suggest that shape components corresponding to atrophy to the CA1 and subiculum HP fields-- where early AD pathology is located-- correlate strongly with robust measures of the cognitive dysfunction that is typical of early AD. Furthermore, the energy function minimized by the shape component optimization technique is shown to be smooth with few local minima, suggesting that the method may be relatively easy to apply in practice.}, owner = {pauly}, timestamp = {2008.09.10}, url = {http://picsl.upenn.edu/caph08/papers/paper16.pdf} } @INPROCEEDINGS{yushkevich08caph, author = {Paul A. Yushkevich and Brian B. Avants and John Pluta and David Minkoff and Stephen Pickup and Weixia Liu and James C. Gee and Murray Grossman and John A. Detre}, title = {A Computational Atlas of the Human Hippocampus from Postmortem Magnetic Resonance Imaging at 9.4 {T}esla}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {56--67}, month = {September}, abstract = {This paper describes the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu Ammonis fields CA1, CA2/3; the dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated.}, url = {http://picsl.upenn.edu/caph08/papers/paper15.pdf} } @INPROCEEDINGS{zhou08caph, author = {Luping Zhou and Richard Hartley and Lei Wang and Paulette Lieby and Nick Barnes}, title = {Regularized Discriminative Direction for Shape Difference Analysis}, booktitle = {MICCAI 2008 Workshop on Computational Anatomy and Physiology of the Hippocampus (CAPH'08)}, year = {2008}, editor = {P. Yushkevich and L. Wang}, pages = {68--75}, month = {September}, abstract = {The ``discriminative direction'' has been proven useful to reveal the subtle difference between two anatomical shape classes. When a shape moves along this direction, its deformation will best manifest the class difference detected by a kernel classifier. However, we observe that such a direction cannot maintain a shape's ``anatomical" correctness, introducing spurious difference. To overcome this drawback, we develop a \textit{regularized} discriminative direction by requiring a shape to conform to its population distribution when it deforms along the discriminative direction. Instead of iterative optimization, an analytic solution is provided to directly work out this direction. Experimental study shows its superior performance in detecting and localizing the difference of hippocampal shapes for sex. The result is supported by other independent research in the same domain.}, url = {http://picsl.upenn.edu/caph08/papers/paper03.pdf} } @comment{jabref-meta: selector_publisher:} @comment{jabref-meta: selector_author:} @comment{jabref-meta: selector_journal:} @comment{jabref-meta: selector_keywords:}