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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/29098066/longitudinal-multi-scale-mapping-of-infant-cortical-folding-using-spherical-wavelets
#1
Dingna Duan, Islem Rekik, Shunren Xia, Weili Lin, John H Gilmore, Dinggang Shen, Gang Li
The dynamic development of brain cognition and motor functions during infancy are highly associated with the rapid changes of the convoluted cortical folding. However, little is known about how the cortical folding, which can be characterized on different scales, develops in the first two postnatal years. In this paper, we propose a curvature-based multi-scale method using spherical wavelets to map the complicated longitudinal changes of cortical folding during infancy. Specifically, we first decompose the cortical curvature map, which encodes the cortical folding information, into multiple spatial-frequency scales, and then measure the scale-specific wavelet power at 6 different scales as quantitative indices of cortical folding degree...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29081898/mahalanobis-distance-for-class-averaging-of-cryo-em-images
#2
Tejal Bhamre, Zhizhen Zhao, Amit Singer
Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically low signal to noise ratio (SNR) of the acquired images. 2D classification of images, followed by class averaging, improves the SNR of the resulting averages, and is used for selecting particles from micrographs and for inspecting the particle images...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29062466/segmentation-of-organs-at-risk-in-thoracic-ct-images-using-a-sharpmask-architecture-and-conditional-random-fields
#3
R Trullo, C Petitjean, S Ruan, B Dubray, D Nie, D Shen
Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/28989563/generative-method-to-discover-emphysema-subtypes-with-unsupervised-learning-using-lung-macroscopic-patterns-lmps-the-mesa-copd-study
#4
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
#5
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
#6
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
#7
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
#8
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/29201284/approximating-principal-genetic-components-of-subcortical-shape
#9
Boris A Gutman, Fabrizio Pizzagalli, Neda Jahanshad, Margaret J Wright, Katie L McMahon, Greig de Zubicaray, Paul M Thompson
Optimal representations of the genetic structure underlying complex neuroimaging phenotypes lie at the heart of our quest to discover the genetic code of the brain. Here, we suggest a strategy for achieving such a representation by decomposing the genetic covariance matrix of complex phenotypes into maximally heritable and genetically independent components. We show that such a representation can be approximated well with eigenvectors of the genetic covariance based on a large family study. Using 520 twin pairs from the QTIM dataset, we estimate 500 principal genetic components of 54,000 vertex-wise shape features representing fourteen subcortical regions...
2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29201283/a-comparison-of-network-definitions-for-detecting-sex-differences-in-brain-connectivity-using-support-vector-machines
#10
George W Hafzalla, Anjanibhargavi Ragothaman, Joshua Faskowitz, Neda Jahanshad, Katie L McMahon, Greig I de Zubicaray, Margaret J Wright, Meredith N Braskie, Gautam Prasad, Paul M Thompson
Human brain connectomics is a rapidly evolving area of research, using various methods to define connections or interactions between pairs of regions. Here we evaluate how the choice of (1) regions of interest, (2) definitions of a connection, and (3) normalization of connection weights to total brain connectivity and region size, affect our calculation of the structural connectome. Sex differences in the structural connectome have been established previously. We study how choices in reconstruction of the connectome affect our ability to classify subjects by sex using a support vector machine (SVM) classifier...
2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29201282/the-impact-of-matching-functional-on-atrophy-measurement-from-geodesic-shooting-in-diffeomorphisms
#11
Greg M Fleishman, Paul M Thompson
Longitudinal registration has been used to map brain atrophy and tissue loss patterns over time, in both healthy and demented subjects. However, we have not seen a thorough application of the geodesic shooting in diffeomorphisms framework for this task. The registration model is complex and several choices must be made that may significantly impact the quality of results. One of these decisions is which image matching functional should drive the registration. We investigate four matching functionals for atrophy quantification using geodesic shooting in diffeomorphisms...
2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29201281/graph-theoretical-approaches-towards-understanding-differences-in-frontoparietal-and-default-mode-networks-in-autism
#12
Brandalyn C Riedel, Neda Jahanshad, Paul M Thompson
Autism Spectrum Disorder is a complex developmental disorder affecting 1 in 68 children in the United States. While the prevalence may be on the rise, we currently lack a firm understanding of the etiology of the disease, and diagnosis is made purely on behavioral observation and informant report. As one method to improve our understanding of the disease, the current study took a systems-level approach by assessing the causal interactions among the frontoparietal and default mode networks using structural covariance of a large Autism dataset...
2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29201280/a-network-approach-to-examining-injury-severity-in-pediatric-tbi
#13
Emily L Dennis, Faisal Rashid, Neda Jahanshad, 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, and can lead to long lasting functional impairment. Many factors influence outcome, but imaging studies examining effects of individual variables are limited by sample size. Roughly 20-40% of hospitalized TBI patients experience seizures, but not all of these patients go on to develop a recurrent seizure disorder. Here we examined differences in structural network connectivity in pediatric patients who had sustained a moderate-severe TBI (msTBI)...
2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29201279/mapping-age-effects-along-fiber-tracts-in-young-adults
#14
Emily L Dennis, Faisal Rashid, Josh Faskowitz, Yan Jin, Katie L McMahon, Greig I de Zubicaray, Nicholas G Martin, Ian B Hickie, Margaret J Wright, Neda Jahanshad, Paul M Thompson
Brain development is a protracted and dynamic process. Many studies have charted the trajectory of white matter development, but here we sought to map these effects in greater detail, based on a large set of fiber tracts automatically extracted from HARDI (high angular resolution diffusion imaging) at 4 tesla. We used autoMATE (automated multi-atlas tract extraction) to extract diffusivity measures along 18 of the brain's major fiber bundles in 667 young adults, aged 18-30. We examined linear and non-linear age effects on diffusivity measures, pointwise along tracts...
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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