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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/28989563/generative-method-to-discover-emphysema-subtypes-with-unsupervised-learning-using-lung-macroscopic-patterns-lmps-the-mesa-copd-study
#1
Jingkuan Song, Jie Yang, Benjamin Smith, Pallavi Balte, Eric A Hoffman, R Graham Barr, Andrew F Laine, Elsa D Angelini
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE) and paraseptal emphysema (PSE). Automated classification methods based on supervised learning are generally based upon the current definition of emphysema subtypes, while unsupervised learning of texture patterns enables the objective discovery of possible new radiological emphysema subtypes. In this work, we use a variant of the Latent Dirichlet Allocation (LDA) model to discover lung macroscopic patterns (LMPs) in an unsupervised way from lung regions that encode emphysematous areas...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28959379/empowering-cortical-thickness-measures-in-clinical-diagnosis-of-alzheimer-s-disease-with-spherical-sparse-coding
#2
Jie Zhang, Yonghui Fan, Qingyang Li, Paul M Thompson, Jieping Ye, Yalin Wang
Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD). Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are not equally atrophied during AD progression. Therefore, feature reduction is generally required to retain only the most relevant features for the final classification...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28890755/architectural-patterns-for-differential-diagnosis-of-proliferative-breast-lesions-from-histopathological-images
#3
L Nguyen, A B Tosun, J L Fine, D L Taylor, S C Chennubhotla
The differential diagnosis of proliferative breast lesions, benign usual ductal hyperplasia (UDH) versus malignant ductal carcinoma in situ (DCIS) is challenging. This involves a pathologist examining histopathologic sections of a biopsy using a light microscope, evaluating tissue structures for their architecture or size, and assessing individual cell nuclei for their morphology. Imposing diagnostic boundaries on features that otherwise exist on a continuum going from benign to atypia to malignant is a challenge...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28804569/dynamic-registration-for-gigapixel-serial-whole-slide-images
#4
Blair J Rossetti, Fusheng Wang, Pengyue Zhang, George Teodoro, Daniel J Brat, Jun Kong
High-throughput serial histology imaging provides a new avenue for the routine study of micro-anatomical structures in a 3D space. However, the emergence of serial whole slide imaging poses a new registration challenge, as the gigapixel image size precludes the direct application of conventional registration techniques. In this paper, we develop a three-stage registration with multi-resolution mapping and propagation method to dynamically produce registered subvolumes from serial whole slide images. We validate our algorithm with gigapixel images of serial brain tumor sections and synthetic image volumes...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28781722/automated-level-set-segmentation-of-histopathologic-cells-with-sparse-shape-prior-support-and-dynamic-occlusion-constraint
#5
Pengyue Zhang, Fusheng Wang, George Teodoro, Yanhui Liang, Daniel Brat, Jun Kong
In this paper, we propose a novel segmentation method for cells in histopathologic images based on a sparse shape prior guided variational level set framework. We automate the cell contour initialization by detecting seeds and deform contours by minimizing a new energy functional that incorporates a shape term involving sparse shape priors, an adaptive contour occlusion penalty term, and a boundary term encouraging contours to converge to strong edges. As a result, our approach is able to accommodate mutual occlusions and detect contours of multiple intersected cells...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28936280/enhancing-diffusion-mri-measures-by-integrating-grey-and-white-matter-morphometry-with-hyperbolic-wasserstein-distance
#6
Wen Zhang, Jie Shi, Jun Yu, Liang Zhan, Paul M Thompson, Yalin Wang
In order to improve the preclinical diagnose of Alzheimer's disease (AD), there is a great deal of interest in analyzing the AD related brain structural changes with magnetic resonance image (MRI) analyses. As the major features, variation of the structural connectivity and the cortical surface morphometry provide different views of structural changes to determine whether AD is present on presymptomatic patients. However, the large scale tensor-valued information and relatively low imaging resolution in diffusion MRI (dMRI) have created huge challenges for analysis...
2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28890754/ct-and-mri-fusion-for-postimplant-prostate-brachytherapy-evaluation
#7
Ehsan Dehghan, Yi Le, Junghoon Lee, Daniel Y Song, Gabor Fichtinger, Jerry L Prince
Postoperative evaluation of prostate brachytherapy is typically performed using CT, which does not have sufficient soft tissue contrast for accurate anatomy delineation. MR-CT fusion enables more accurate localization of both anatomy and implanted radioactive seeds, and hence, improves the accuracy of postoperative dosimetry. We propose a method for automatic registration of MR and CT images without a need for manual initialization. Our registration method employs a point-to-volume registration scheme during which localized seeds in the CT images, produced by commercial treatment planning systems as part of the standard of care, are rigidly registered to preprocessed MRI images...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28479960/evaluation-of-numerical-techniques-for-solving-the-current-injection-problem-in-biological-tissues
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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