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Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro

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https://www.readbyqxmd.com/read/27695603/intensity-inhomogeneity-correction-of-macular-oct-using-n3-and-retinal-flatspace
#1
Andrew Lang, Aaron Carass, Bruno M Jedynak, Sharon D Solomon, Peter A Calabresi, Jerry L Prince
As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images. This problem negatively affects the performance of segmentation algorithms and little prior work has been done to correct this data...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27499829/applying-sparse-coding-to-surface-multivariate-tensor-based-morphometry-to-predict-future-cognitive-decline
#2
Jie Zhang, Cynthia Stonnington, Qingyang Li, Jie Shi, Robert J Bauer, Boris A Gutman, Kewei Chen, Eric M Reiman, Paul M Thompson, Jieping Ye, Yalin Wang
Alzheimer's disease (AD) is a progressive brain disease. Accurate diagnosis of AD and its prodromal stage, mild cognitive impairment, is crucial for clinical trial design. There is also growing interests in identifying brain imaging biomarkers that help evaluate AD risk presymptomatically. Here, we applied a recently developed multivariate tensor-based morphometry (mTBM) method to extract features from hippocampal surfaces, derived from anatomical brain MRI. For such surface-based features, the feature dimension is usually much larger than the number of subjects...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27499828/morphometric-analysis-of-hippocampus-and-lateral-ventricle-reveals-regional-difference-between-cognitively-stable-and-declining-persons
#3
Wen Zhang, Jie Shi, Cynthia Stonnington, Robert J Bauer, Boris A Gutman, Kewei Chen, Paul M Thompson, Eric M Reiman, Richard J Caselli, Yalin Wang
Alzheimers disease (AD) is a progressive neurodegenerative disease most prevalent in the elderly. Distinguishing disease-related memory decline from normal age-related memory decline has been clinically difficult due to the subtlety of cognitive change during the preclinical stage of AD. In contrast, sensitive biomarkers derived from in vivo neuroimaging data could improve the early identification of AD. In this study, we employed a morphometric analysis in the hippocampus and lateral ventricle. A novel group-wise template-based segmentation algorithm was developed for ventricular segmentation...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27390615/phenotypic-characterization-of-breast-invasive-carcinoma-via-transferable-tissue-morphometric-patterns-learned-from-glioblastoma-multiforme
#4
Ju Han, Gerald V Fontenay, Yunfu Wang, Jian-Hua Mao, Hang Chang
Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27668065/fully-convolutional-networks-for-multi-modality-isointense-infant-brain-image-segmentation
#5
Dong Nie, Li Wang, Yaozong Gao, Dinggang Shen
The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information...
2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26401225/a-statistical-approach-to-incorporate-multiple-ecg-or-eeg-recordings-with-artifactual-variability-into-inverse-solutions
#6
J Coll-Font, B Erem, P Štóvíček, D H Brooks
Inverse methods for localization and characterization of cardiac and brain sources from ECG and EEG signals are notoriously ill-conditioned and thus sensitive to SNR in the measurements. Multiple recordings of the same underlying phenomenon are often available, but are contaminated by unmodeled correlated noise such as heart motion from respiration or superposition of atrial activation or on-going EEG in the case of inter-ictal spikes or evoked response in EEG. We address here the open question of how best to incorporate these multiple recordings, comparing standard ensemble averaging, a multichannel non-linear spline-based average designed to be less sensitive to timing variations from motion or modulation, and a probalistic inverse incorporating a data-driven model of the noise correlation and using all recordings jointly...
April 16, 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27563391/probabilistic-fiber-tracking-using-a-modified-lasso-bootstrap-method
#7
Chuyang Ye, Jeffrey Glaister, Jerry L Prince
Diffusion MRI (dMRI) provides a noninvasive tool for investigating white matter tracts. Probabilistic fiber tracking has been proposed to represent the fiber structures as 3D streamlines while taking the uncertainty introduced by noise into account. In this paper, we propose a probabilistic fiber tracking method based on bootstrapping a multi-tensor model with a fixed tensor basis. The fiber orientation (FO) estimation is formulated as a Lasso problem. Then by resampling the residuals calculated using a modified Lasso estimator to create synthetic diffusion signals, a distribution of FOs is estimated...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27403233/disjunctive-normal-shape-models
#8
Nisha Ramesh, Fitsum Mesadi, Mujdat Cetin, Tolga Tasdizen
A novel implicit parametric shape model is proposed for segmentation and analysis of medical images. Functions representing the shape of an object can be approximated as a union of N polytopes. Each polytope is obtained by the intersection of M half-spaces. The shape function can be approximated as a disjunction of conjunctions, using the disjunctive normal form. The shape model is initialized using seed points defined by the user. We define a cost function based on the Chan-Vese energy functional. The model is differentiable, hence, gradient based optimization algorithms are used to find the model parameters...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27284387/multi-resolution-statistical-analysis-on-graph-structured-data-in-neuroimaging
#9
Won Hwa Kim, Vikas Singh, Moo K Chung, Nagesh Adluru, Barbara B Bendlin, Sterling C Johnson
Statistical data analysis plays a major role in discovering structural and functional imaging phenotypes for mental disorders such as Alzheimer's disease (AD). The goal here is to identify, ideally early on, which regions in the brain show abnormal variations with a disorder. To make the method more sensitive, we rely on a multi-resolutional perspective of the given data. Since the underlying imaging data (such as cortical surfaces and connectomes) are naturally represented in the form of weighted graphs which lie in a non-Euclidean space, we introduce recent work from the harmonics literature to derive an effective multi-scale descriptor using wavelets on graphs that characterize the local context at each data point...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26730456/multiscale-tensor-anisotropic-filtering-of-fluorescence-microscopy-for-denoising-microvasculature
#10
V B S Prasath, R Pelapur, O V Glinskii, V V Glinsky, V H Huxley, K Palaniappan
Fluorescence microscopy images are contaminated by noise and improving image quality without blurring vascular structures by filtering is an important step in automatic image analysis. The application of interest here is to automatically extract the structural components of the microvascular system with accuracy from images acquired by fluorescence microscopy. A robust denoising process is necessary in order to extract accurate vascular morphology information. For this purpose, we propose a multiscale tensor with anisotropic diffusion model which progressively and adaptively updates the amount of smoothing while preserving vessel boundaries accurately...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26682015/covariance-estimation-using-conjugate-gradient-for-3d-classification-in-cryo-em
#11
Joakim Andén, Eugene Katsevich, Amit Singer
Classifying structural variability in noisy projections of biological macromolecules is a central problem in Cryo-EM. In this work, we build on a previous method for estimating the covariance matrix of the three-dimensional structure present in the molecules being imaged. Our proposed method allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. We evaluate its performance on a synthetic dataset and an experimental dataset obtained by imaging a 70S ribosome complex...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26677402/orthogonal-matrix-retrieval-in-cryo-electron-microscopy
#12
Tejal Bhamre, Teng Zhang, Amit Singer
In single particle reconstruction (SPR) from cryo-electron microscopy (EM), the 3D structure of a molecule needs to be determined from its 2D projection images taken at unknown viewing directions. Zvi Kam showed already in 1980 that the autocorrelation function of the 3D molecule over the rotation group SO(3) can be estimated from 2D projection images whose viewing directions are uniformly distributed over the sphere. The autocorrelation function determines the expansion coefficients of the 3D molecule in spherical harmonics up to an orthogonal matrix of size (2l + 1) × (2l + 1) for each l = 0,1,2,…...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26617955/correcting-inhomogeneity-induced-distortion-in-fmri-using-non-rigid-registration
#13
Micah C Chambers, Chitresh Bhushan, Justin P Haldar, Richard M Leahy, David W Shattuck
Magnetic field inhomogeneities in echo planar images (EPI) can cause large distortion in the phase encoding dimension. In functional MRI (fMRI), this distortion can shift activation loci, increase inter subject variability, and reduce statistical power during group analysis. Distortion correction methods that make use of acquired magnetic field maps have been developed, however, field maps are not always acquired or may not be available to researchers. An alternative approach, which we pursue in this paper, is to estimate the distortion retrospectively by spatially registering the EPI to a structural MRI...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26413211/medial-demons-registration-localizes-the-degree-of-genetic-influence-over-subcortical-shape-variability-an-n-1480-meta-analysis
#14
Boris A Gutman, Neda Jahanshad, Christopher R K Ching, Yalin Wang, Peter V Kochunov, Thomas E Nichols, Paul M Thompson
We present a multi-cohort shape heritability study, extending the fast spherical demons registration to subcortical shapes via medial modeling. A multi-channel demons registration based on vector spherical harmonics is applied to medial and curvature features, while controlling for metric distortion. We registered and compared seven subcortical structures of 1480 twins and siblings from the Queensland Twin Imaging Study and Human Connectome Project: Thalamus, Caudate, Putamen, Pallidum, Hippocampus, Amygdala, and Nucleus Accumbens...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26413210/genetic-analysis-of-structural-brain-connectivity-using-dicccol-models-of-diffusion-mri-in-522-twins
#15
Dajiang Zhu, Liang Zhan, Joshua Faskowitz, Madelaine Daianu, Neda Jahanshad, Greig I de Zubicaray, Katie L McMahon, Nicholas G Martin, Margaret J Wright, Paul M Thompson
Genetic and environmental factors affect white matter connectivity in the normal brain, and they also influence diseases in which brain connectivity is altered. Little is known about genetic influences on brain connectivity, despite wide variations in the brain's neural pathways. Here we applied the "DICCCOL" framework to analyze structural connectivity, in 261 twin pairs (522 participants, mean age: 21.8 y ± 2.7SD). We encoded connectivity patterns by projecting the white matter (WM) bundles of all "DICCCOLs" as a tracemap (TM)...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26413209/detecting-genetic-risk-factors-for-alzheimer-s-disease-in-whole-genome-sequence-data-via-lasso-screening
#16
Tao Yang, Jie Wang, Qian Sun, Derrek P Hibar, Neda Jahanshad, Li Liu, Yalin Wang, Liang Zhan, Paul M Thompson, Jieping Ye
Genetic factors play a key role in Alzheimer's disease (AD). The Alzheimer's Disease Neuroimaging Initiative (ADNI) whole genome sequence (WGS) data offers new power to investigate mechanisms of AD by combining entire genome sequences with neuroimaging and clinical data. Here we explore the ADNI WGS SNP (single nucleotide polymorphism) data in depth and extract approximately six million valid SNP features. We investigate imaging genetics associations using Lasso regression-a widely used sparse learning technique...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26413208/information-theoretic-characterization-of-blood-panel-predictors-for-brain-atrophy-and-cognitive-decline-in-the-elderly
#17
Sarah K Madsen, Greg Ver Steeg, Adam Mezher, Neda Jahanshad, Talia M Nir, Xue Hua, Boris A Gutman, Aram Galstyan, Paul M Thompson
Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we used a novel unsupervised machine learning approach to evaluate an extensive list of more than 200 potential brain, blood and cerebrospinal fluid (CSF)-based predictors of cognitive decline. The method, called CorEx, discovers groups of variables with high multivariate mutual information and then constructs latent factors that explain these correlations...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26413207/mapping-abnormal-subcortical-brain-morphometry-in-an-elderly-hiv-cohort
#18
Benjamin S C Wade, Victor G Valcour, Lauren Wendelken-Riegelhaupt, Pardis Esmaeili-Firidouni, Shantanu H Joshi, Yalin Wang, Paul M Thompson
Over 50% of HIV+ individuals exhibit neurocognitive impairment and subcortical atrophy, but the pattern of brain abnormalities associated with HIV is still poorly understood. Using parametric surface-based shape analyses, we mapped the 3D profile of subcortical morphometry in 63 HIV+ participants and 31 uninfected controls. The thalamus, corpus striatum, hippocampus, amygdala, brainstem, callosum and ventricles were segmented from brain MRIs. To investigate subcortical shape, we analyzed the Jacobian determinant (JD) and radial distances (RD) for structure surfaces...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26413206/white-matter-integrity-in-traumatic-brain-injury-effects-of-permissible-fiber-turning-angle
#19
Emily L Dennis, Yan Jin, Claudia Kernan, Talin Babikian, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher C Giza, Robert F Asarnow, Paul M Thompson
Traumatic brain injury (TBI) is the leading cause of death and disability in children. Diffusion weighted imaging (DWI) methods have been shown to be especially sensitive to white matter abnormalities in TBI. We used our newly developed autoMATE algorithm (automated multi-atlas tract extraction) to map altered WM integrity in TBI. Even so, tractography methods include a free parameter that limits the maximum permissible turning angles for extracted fibers, with little investigation of how this may affect statistical group comparisons...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/26413205/spectral-graph-theory-and-graph-energy-metrics-show-evidence-for-the-alzheimer-s-disease-disconnection-syndrome-in-apoe-4-risk-gene-carriers
#20
Madelaine Daianu, Adam Mezher, Neda Jahanshad, Derrek P Hibar, Talia M Nir, Clifford R Jack, Michael W Weiner, Matt A Bernstein, Paul M Thompson
Our understanding of network breakdown in Alzheimer's disease (AD) is likely to be enhanced through advanced mathematical descriptors. Here, we applied spectral graph theory to provide novel metrics of structural connectivity based on 3-Tesla diffusion weighted images in 42 AD patients and 50 healthy controls. We reconstructed connectivity networks using whole-brain tractography and examined, for the first time here, cortical disconnection based on the graph energy and spectrum. We further assessed supporting metrics - link density and nodal strength - to better interpret our results...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
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