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

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https://www.readbyqxmd.com/read/29129964/a-likelihood-free-approach-for-characterizing-heterogeneous-diseases-in-large-scale-studies
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
(no author information available yet)
No abstract text is available yet for this article.
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26223048/multiple-orderings-of-events-in-disease-progression
#15
Alexandra L Young, Neil P Oxtoby, Jonathan Huang, Razvan V Marinescu, Pankaj Daga, David M Cash, Nick C Fox, Sebastien Ourselin, Jonathan M Schott, Daniel C Alexander
The event-based model constructs a discrete picture of disease progression from cross-sectional data sets, with each event corresponding to a new biomarker becoming abnormal. However, it relies on the assumption that all subjects follow a single event sequence. This is a major simplification for sporadic disease data sets, which are highly heterogeneous, include distinct subgroups, and contain significant proportions of outliers. In this work we relax this assumption by considering two extensions to the event-based model: a generalised Mallows model, which allows subjects to deviate from the main event sequence, and a Dirichlet process mixture of generalised Mallows models, which models clusters of subjects that follow different event sequences, each of which has a corresponding variance...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26223047/ground-truth-for-diffusion-mri-in-cancer-a-model-based-investigation-of-a-novel-tissue-mimetic-material
#16
Damien J McHugh, Fenglei Zhou, Penny L Hubbard Cristinacce, Josephine H Naish, Geoffrey J M Parker
This work presents preliminary results on the development, characterisation, and use of a novel physical phantom designed as a simple mimic of tumour cellular structure, for diffusion-weighted magnetic resonance imaging (DW-MRI) applications. The phantom consists of a collection of roughly spherical, micron-sized core-shell polymer 'cells', providing a system whose ground truth microstructural properties can be determined and compared with those obtained from modelling the DW-MRI signal. A two-compartment analytic model combining restricted diffusion inside a sphere with hindered extracellular diffusion was initially investigated through Monte Carlo diffusion simulations, allowing a comparison between analytic and simulated signals...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221718/joint-6d-k-q-space-compressed-sensing-for-accelerated-high-angular-resolution-diffusion-mri
#17
Jian Cheng, Dinggang Shen, Peter J Basser, Pew-Thian Yap
High Angular Resolution Diffusion Imaging (HARDI) avoids the Gaussian. diffusion assumption that is inherent in Diffusion Tensor Imaging (DTI), and is capable of characterizing complex white matter micro-structure with greater precision. However, HARDI methods such as Diffusion Spectrum Imaging (DSI) typically require significantly more signal measurements than DTI, resulting in prohibitively long scanning times. One of the goals in HARDI research is therefore to improve estimation of quantities such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF) with a limited number of diffusion-weighted measurements...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221717/coupled-stable-overlapping-replicator-dynamics-for-multimodal-brain-subnetwork-identification
#18
Burak Yoldemir, Bernard Ng, Rafeef Abugharbieh
Combining imaging modalities to synthesize their inherent strengths provides a promising means for improving brain subnetwork identification. We propose a multimodal integration technique based on a sex-differentiated formulation of replicator dynamics for identifying subnetworks of brain regions that exhibit high inter-connectivity both functionally and structurally. Our method has a number of desired properties, namely, it can operate on weighted graphs derived from functional magnetic resonance imaging (tMRI) and diffusion MRI (dMRI) data, allows for subnetwork overlaps, has an intrinsic criterion for setting the number of subnetworks, and provides statistical control on false node inclusion in the identified subnetworks via the incorporation of stability selection...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221716/tree-encoded-conditional-random-fields-for-image-synthesis
#19
Amod Jog, Aaron Carass, Dzung L Pham, Jerry L Prince
Magnetic resonance imaging (MRI) is the dominant modality for neuroimaging in clinical and research domains. The tremendous versatility of MRI as a modality can lead to large variability in terms of image contrast, resolution, noise, and artifacts. Variability can also manifest itself as missing or corrupt imaging data. Image synthesis has been recently proposed to homogenize and/or enhance the quality of existing imaging data in order to make them more suitable as consistent inputs for processing. We frame the image synthesis problem as an inference problem on a 3-D continuous-valued conditional random field (CRF)...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221715/construction-of-an-unbiased-spatio-temporal-atlas-of-the-tongue-during-speech
#20
Jonghye Woo, Fangxu Xing, Junghoon Lee, Maureen Stone, Jerry L Prince
Quantitative characterization and comparison of tongue motion during speech and swallowing present fundamental challenges because of striking variations in tongue structure and motion across subjects. A reliable and objective description of the dynamics tongue motion requires the consistent integration of inter-subject variability to detect the subtle changes in populations. To this end, in this work, we present an approach to constructing an unbiased spatio-temporal atlas of the tongue during speech for the first time, based on cine-MRI from twenty two normal subjects...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
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