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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/28479960/evaluation-of-numerical-techniques-for-solving-the-current-injection-problem-in-biological-tissues
#1
Damon E Hyde, Moritz Dannhauer, Simon K Warfield, Rob MacLeod, Dana H Brooks
Accurate computational modeling of electric fields in the human head has become important in clinical research to study or influence brain functionality. While existing numerical approaches have been evaluated against simple geometries with known closed form solutions, the relationship between these approaches in more complex geometries has not been studied. Here, we compare the three most commonly used approaches for bioelectric modeling: the finite element method (FEM), the finite difference method (FDM), and the boundary element method (BEM)...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28479959/optimizing-stimulus-patterns-for-dense-array-tdcs-with-fewer-sources-than-electrodes-using-a-branch-and-bound-algorithm
#2
Seyhmus Guler, Moritz Dannhauer, Burak Erem, Rob Macleod, Don Tucker, Sergei Turovets, Phan Luu, Waleed Meleis, Dana H Brooks
Dense array transcranial direct current stimulation (tDCS) has become of increasing interest as a noninvasive modality to modulate brain function. To target a particular brain region of interest (ROI), using a dense electrode array placed on the scalp, the current injection pattern can be appropriately optimized. Previous optimization methods have assumed availability of individually controlled current sources for each non-reference electrode. This may be costly and impractical in a clinical setting. However, using fewer current sources than electrodes results in a non-convex combinatorial optimization problem...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28392891/robust-cell-segmentation-for-histological-images-of-glioblastoma
#3
Jun Kong, Pengyue Zhang, Yanhui Liang, George Teodoro, Daniel J Brat, Fusheng Wang
Glioblastoma (GBM) is a malignant brain tumor with uniformly dismal prognosis. Quantitative analysis of GBM cells is an important avenue to extract latent histologic disease signatures to correlate with molecular underpinnings and clinical outcomes. As a prerequisite, a robust and accurate cell segmentation is required. In this paper, we present an automated cell segmentation method that can satisfactorily address segmentation of overlapped cells commonly seen in GBM histology specimens. This method first detects cells with seed connectivity, distance constraints, image edge map, and a shape-based voting image...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28090247/optimal-parameter-map-estimation-for-shape-representation-a-generative-approach
#4
Shireen Y Elhabian, Praful Agrawal, Ross T Whitaker
Probabilistic label maps are a useful tool for important medical image analysis tasks such as segmentation, shape analysis, and atlas building. Existing methods typically rely on blurred signed distance maps or smoothed label maps to model uncertainties and shape variabilities, which do not conform to any generative model or estimation process, and are therefore suboptimal. In this paper, we propose to learn probabilistic label maps using a generative model on given set of binary label maps. The proposed approach generalizes well on unseen data while simultaneously capturing the variability in the training samples...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28090246/bayesian-covariate-selection-in-mixed-effects-models-for-longitudinal-shape-analysis
#5
Prasanna Muralidharan, James Fishbaugh, Eun Young Kim, Hans J Johnson, Jane S Paulsen, Guido Gerig, P Thomas Fletcher
The goal of longitudinal shape analysis is to understand how anatomical shape changes over time, in response to biological processes, including growth, aging, or disease. In many imaging studies, it is also critical to understand how these shape changes are affected by other factors, such as sex, disease diagnosis, IQ, etc. Current approaches to longitudinal shape analysis have focused on modeling age-related shape changes, but have not included the ability to handle covariates. In this paper, we present a novel Bayesian mixed-effects shape model that incorporates simultaneous relationships between longitudinal shape data and multiple predictors or covariates to the model...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27974952/derivation-of-a-test-statistic-for-emphysema-quantification
#6
Gonzalo Vegas-Sanchez-Ferrero, George Washko, Farbod N Rahaghi, Maria J Ledesma-Carbayo, R San José Estépar
Density masking is the de-facto quantitative imaging phenotype for emphysema that is widely used by the clinical community. Density masking defines the burden of emphysema by a fixed threshold, usually between -910 HU and -950 HU, that has been experimentally validated with histology. In this work, we formalized emphysema quantification by means of statistical inference. We show that a non-central Gamma is a good approximation for the local distribution of image intensities for normal and emphysema tissue. We then propose a test statistic in terms of the sample mean of a truncated non-central Gamma random variable...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27974951/automated-agatston-score-computation-in-a-large-dataset-of-non-ecg-gated-chest-computed-tomography
#7
Germán González, George R Washko, Raúl San José Estépar
The Agatston score, computed from ECG-gated computed tomography (CT), is a well established metric of coronary artery disease. It has been recently shown that the Agatston score computed from chest CT (non ECG-gated) studies is highly correlated with the Agatston score computed from cardiac CT scans. In this work we present an automated method to compute the Agatston score from chest CT images. Coronary arteries calcifications (CACs) are defined as voxels contained within the coronary arteries with a value greater or equal to 130 Hounsfield Units (HU)...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27917260/integrative-bayesian-analysis-of-neuroimaging-genetic-data-through-hierarchical-dimension-reduction
#8
S Azadeh, B P Hobbs, L Ma, D A Nielsen, F G Moeller, V Baladandayuthapani
Advances in neuromedicine have emerged from endeavors to elucidate the distinct genetic factors that influence the changes in brain structure that underlie various neurological conditions. We present a framework for examining the extent to which genetic factors impact imaging phenotypes described by voxel-wise measurements organized into collections of functionally relevant regions of interest (ROIs) that span the entire brain. Statistically, the integration of neuroimaging and genetic data is challenging. Because genetic variants are expected to impact different regions of the brain, an appropriate method of inference must simultaneously account for spatial dependence and model uncertainty...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27695603/intensity-inhomogeneity-correction-of-macular-oct-using-n3-and-retinal-flatspace
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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/28435514/automatic-muscle-perimysium-annotation-using-deep-convolutional-neural-network
#15
Manish Sapkota, Fuyong Xing, Hai Su, Lin Yang
Diseased skeletal muscle expresses mononuclear cell infiltration in the regions of perimysium. Accurate annotation or segmentation of perimysium can help biologists and clinicians to determine individualized patient treatment and allow for reasonable prognostication. However, manual perimysium annotation is time consuming and prone to inter-observer variations. Meanwhile, the presence of ambiguous patterns in muscle images significantly challenge many traditional automatic annotation algorithms. In this paper, we propose an automatic perimysium annotation algorithm based on deep convolutional neural network (CNN)...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28101301/nuclei-segmentation-via-sparsity-constrained-convolutional-regression
#16
Yin Zhou, Hang Chang, Kenneth E Barner, Bahram Parvin
Automated profiling of nuclear architecture, in histology sections, can potentially help predict the clinical outcomes. However, the task is challenging as a result of nuclear pleomorphism and cellular states (e.g., cell fate, cell cycle), which are compounded by the batch effect (e.g., variations in fixation and staining). Present methods, for nuclear segmentation, are based on human-designed features that may not effectively capture intrinsic nuclear architecture. In this paper, we propose a novel approach, called sparsity constrained convolutional regression (SCCR), for nuclei segmentation...
April 2015: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/27924189/unsupervised-shape-prior-modeling-for-cell-segmentation-in-neuroendocrine-tumor
#17
Fuyong Xing, Lin Yang
Automated and accurate cell segmentation provides support for many quantitative analyses on digitized neuroendocrine tumor (NET) images. It is a challenging task due to complex variations of cell characteristics. In this paper, we incorporate unsupervised shape priors into an efficient repulsive deformable model for automated cell segmentation on NET images. Unlike other supervised learning based shape models, which usually require a large number of annotated data for training, the proposed algorithm is an unsupervised approach that applies group similarity to shape constraints to avoid any labor intensive annotation...
April 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
#18
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
#19
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
#20
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
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