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Information Processing in Medical Imaging: Proceedings of the ... Conference

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https://www.readbyqxmd.com/read/29743804/hierarchical-region-network-sparsity-for-high-dimensional-inference-in-brain-imaging
#1
Danilo Bzdok, Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion
Structured sparsity penalization has recently improved statistical models applied to high-dimensional data in various domains. As an extension to medical imaging, the present work incorporates priors on network hierarchies of brain regions into logistic-regression to distinguish neural activity effects. These priors bridge two separately studied levels of brain architecture: functional segregation into regions and functional integration by networks. Hierarchical region-network priors are shown to better classify and recover 18 psychological tasks than other sparse estimators...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/29657509/a-tensor-statistical-model-for-quantifying-dynamic-functional-connectivity
#2
Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Jin Yan, Guorong Wu
Functional connectivity (FC) has been widely investigated in many imaging-based neuroscience and clinical studies. Since functional Magnetic Resonance Image (MRI) signal is just an indirect reflection of brain activity, it is difficult to accurately quantify the FC strength only based on signal correlation. To address this limitation, we propose a learning-based tensor model to derive high sensitivity and specificity connectome biomarkers at the individual level from resting-state fMRI images. First, we propose a learning-based approach to estimate the intrinsic functional connectivity...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/29503515/riccati-regularized-precision-matrices-for-neuroimaging
#3
Nicolas Honnorat, Christos Davatzikos
The introduction of graph theory in neuroimaging has provided invaluable tools for the study of brain connectivity. These methods require the definition of a graph, which is typically derived by estimating the effective connectivity between brain regions through the optimization of an ill-posed inverse problem. Considerable efforts have been devoted to the development of methods extracting sparse connectivity graphs. The present paper aims at highlighting the benefits of an alternative approach. We investigate low-rank L2 regularized matrices recently introduced under the denomination of Riccati regularized precision matrices...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/29398876/kernel-methods-for-riemannian-analysis-of-robust-descriptors-of-the-cerebral-cortex
#4
Suyash P Awate, Richard M Leahy, Anand A Joshi
Typical cerebral cortical analyses rely on spatial normalization and are sensitive to misregistration arising from partial homologies between subject brains and local optima in nonlinear registration. In contrast, we use a descriptor of the 3D cortical sheet (jointly modeling folding and thickness) that is robust to misregistration. Our histogram-based descriptor lies on a Riemannian manifold . We propose new regularized nonlinear methods for (i) detecting group differences, using a Mercer kernel with an implicit lifting map to a reproducing kernel Hilbert space, and (ii) regression against clinical variables, using kernel density estimation ...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/29391767/frequency-diffeomorphisms-for-efficient-image-registration
#5
Miaomiao Zhang, Ruizhi Liao, Adrian V Dalca, Esra A Turk, Jie Luo, P Ellen Grant, Polina Golland
This paper presents an efficient algorithm for large deformation diffeomorphic metric mapping (LDDMM) with geodesic shooting for image registration. We introduce a novel finite dimensional Fourier representation of diffeomorphic deformations based on the key fact that the high frequency components of a diffeomorphism remain stationary throughout the integration process when computing the deformation associated with smooth velocity fields. We show that manipulating high dimensional diffeomorphisms can be carried out entirely in the bandlimited space by integrating the nonstationary low frequency components of the displacement field...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/29379264/population-based-image-imputation
#6
Adrian V Dalca, Katherine L Bouman, William T Freeman, Natalia S Rost, Mert R Sabuncu, Polina Golland
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images. Highly specialized or application-specific algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/29129964/a-likelihood-free-approach-for-characterizing-heterogeneous-diseases-in-large-scale-studies
#7
Jenna Schabdach, William M Wells, Michael Cho, Kayhan N Batmanghelich
We propose a non-parametric approach for characterizing heterogeneous diseases in large-scale studies. We target diseases where multiple types of pathology present simultaneously in each subject and a more severe disease manifests as a higher level of tissue destruction. For each subject, we model the collection of local image descriptors as samples generated by an unknown subject-specific probability density. Instead of approximating the probability density via a parametric family, we propose to side step the parametric inference by directly estimating the divergence between subject densities...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/29081630/estimation-of-clean-and-centered-brain-network-atlases-using-diffusive-shrinking-graphs-with-application-to-developing-brains
#8
Islem Rekik, Gang Li, Weili Lin, Dinggang Shen
Many methods have been developed to spatially normalize a population of brain images for estimating a mean image as a population-average atlas. However, methods for deriving a network atlas from a set of brain networks sitting on a complex manifold are still absent. Learning how to average brain networks across subjects constitutes a key step in creating a reliable mean representation of a population of brain networks, which can be used to spot abnormal deviations from the healthy network atlas. In this work, we propose a novel network atlas estimation framework, which guarantees that the produced network atlas is clean (for tuning down noisy measurements) and well-centered (for being optimally close to all subjects and representing the individual traits of each subject in the population)...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/29075089/exact-topological-inference-for-paired-brain-networks-via-persistent-homology
#9
Moo K Chung, Victoria Vilalta-Gil, Hyekyoung Lee, Paul J Rathouz, Benjamin B Lahey, David H Zald
We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology. The framework is general enough for dealing with any type of paired images such as twins, multimodal and longitudinal images. The exact nonparametric statistical inference procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests. The proposed method computes the exact probability in quadratic time while the permutation tests require exponential time...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/28947871/hfprm-hierarchical-functional-principal-regression-model-for-diffusion-tensor-image-bundle-statistics
#10
Jingwen Zhang, Chao Huang, Joseph G Ibrahim, Shaili Jha, Rebecca C Knickmeyer, John H Gilmore, Martin Styner, Hongtu Zhu
Diffusion-weighted magnetic resonance imaging (MRI) provides a unique approach to understand the geometric structure of brain fiber bundles and to delineate the diffusion properties across subjects and time. It can be used to identify structural connectivity abnormalities and helps to diagnose brain-related disorders. The aim of this paper is to develop a novel, robust, and efficient dimensional reduction and regression framework, called hierarchical functional principal regression model (HFPRM), to effectively correlate high-dimensional fiber bundle statistics with a set of predictors of interest, such as age, diagnosis status, and genetic markers...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/28943732/topographic-regularity-for-tract-filtering-in-brain-connectivity
#11
Junyan Wang, Dogu Baran Aydogan, Rohit Varma, Arthur W Toga, Yonggang Shi
The preservation of the spatial relationships among axonal pathways has long been studied and known to be critical for many functions of the brain. Being a fundamental property of the brain connections, there is an intuitive understanding of topographic regularity in neuroscience but yet to be systematically explored in connectome imaging research. In this work, we propose a general mathematical model for topographic regularity of fiber bundles that is consistent with its neuroanatomical understanding. Our model is based on a novel group spectral graph analysis (GSGA) framework motivated by spectral graph theory and tensor decomposition...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/28943731/multi-source-multi-target-dictionary-learning-for-prediction-of-cognitive-decline
#12
Jie Zhang, Qingyang Li, Richard J Caselli, Paul M Thompson, Jieping Ye, Yalin Wang
Alzheimer's Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/28943730/conditional-local-distance-correlation-for-manifold-valued-data
#13
Wenliang Pan, Xueqin Wang, Canhong Wen, Martin Styner, Hongtu Zhu
Manifold-valued data arises frequently in medical imaging, surface modeling, computational biology, and computer vision, among many others. The aim of this paper is to introduce a conditional local distance correlation measure for characterizing a nonlinear association between manifold-valued data, denoted by X , and a set of variables (e.g., diagnosis), denoted by Y , conditional on the other set of variables (e.g., gender and age), denoted by Z . Our nonlinear association measure is solely based on the distance of the space that X , Y , and Z are resided, avoiding both specifying any parametric distribution and link function and projecting data to local tangent planes...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/28867917/identifying-associations-between-brain-imaging-phenotypes-and-genetic-factors-via-a-novel-structured-scca-approach
#14
Lei Du, Tuo Zhang, Kefei Liu, Jingwen Yan, Xiaohui Yao, Shannon L Risacher, Andrew J Saykin, Junwei Han, Lei Guo, Li Shen
Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/28848302/predicting-interrelated-alzheimer-s-disease-outcomes-via-new-self-learned-structured-low-rank-model
#15
Xiaoqian Wang, Kefei Liu, Jingwen Yan, Shannon L Risacher, Andrew J Saykin, Li Shen, Heng Huang
Alzheimer's disease (AD) is a progressive neurodegenerative disorder. As the prodromal stage of AD, Mild Cognitive Impairment (MCI) maintains a good chance of converting to AD. How to efficaciously detect this conversion from MCI to AD is significant in AD diagnosis. Different from standard classification problems where the distributions of classes are independent, the AD outcomes are usually interrelated (their distributions have certain overlaps). Most of existing methods failed to examine the interrelations among different classes, such as AD, MCI conversion and MCI non-conversion...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26257500/sampling-from-determinantal-point-processes-for-scalable-manifold-learning
#16
Christian Wachinger, Polina Golland
High computational costs of manifold learning prohibit its application for large datasets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the Nyström method. The two main challenges that arise are: (i) the landmarks selected in non-Euclidean geometries must result in a low reconstruction error, (ii) the graph constructed from sparsely sampled landmarks must approximate the manifold well. We propose to sample the landmarks from determinantal distributions on non-Euclidean spaces...
July 2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26257499/keypoint-transfer-segmentation
#17
C Wachinger, M Toews, G Langs, W Wells, P Golland
We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm...
July 2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26257498/generative-method-to-discover-genetically-driven-image-biomarkers
#18
Nematollah K Batmanghelich, Ardavan Saeedi, Michael Cho, Raul San Jose Estepar, Polina Golland
We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint generative model of image and genetic markers to identify disease subtypes guided by known genetic influences...
July 2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26236122/a-riemannian-framework-for-intrinsic-comparison-of-closed-genus-zero-shapes
#19
Boris Gutman, Thomas Fletcher, M Jorge Cardoso, Greg Fleishman, Marco Lorenzi, Paul Thompson, Sebastien Ourselin
We present a framework for intrinsic comparison of surface metric structures and curvatures. This work parallels the work of Kurtek et al. on parameterization-invariant comparison of genus zero shapes. Here, instead of comparing the embedding of spherically parameterized surfaces in space, we focus on the first fundamental form. To ensure that the distance on spherical metric tensor fields is invariant to parameterization, we apply the conjugation-invariant metric arising from the L(2) norm on symmetric positive definite matrices...
July 2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26419020/proceedings-of-the-24th-international-information-processing-in-medical-imaging-conference-ipmi-2015-june-28-july-3-2015-isle-of-skye-united-kingdom
#20
(no author information available yet)
No abstract text is available yet for this article.
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
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