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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/30123414/hippocampus-morphometry-study-on-pathology-confirmed-alzheimer-s-disease-patients-with-surface-multivariate-morphometry-statistics
#1
Jianfeng Wu, Jie Zhang, Jie Shi, Kewei Chen, Richard J Caselli, Eric M Reiman, Yalin Wang
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases in elderly and the incidence of this disease is increasing with older ages. One of the hallmarks of AD is the accumulation of beta-amyloid plaques (A β ) in human brains. Most of prior brain imaging researchers used the clinical symptom based diagnosis without the confirmation of imaging or fluid A β information. In this work, we study hippocampus morphometry on a cohort consisting of A β positive AD ( N = 151) and matched A β negative cognitively unimpaired subjects ( N = 271) with A β positivity determined via florbetapir PET...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/30093961/using-deep-neural-networks-for-radiogenomic-analysis
#2
Nova F Smedley, William Hsu
Radiogenomic studies have suggested that biological heterogeneity of tumors is reflected radiographically through visible features on magnetic resonance (MR) images. We apply deep learning techniques to map between tumor gene expression profiles and tumor morphology in pre-operative MR studies of glioblastoma patients. A deep autoencoder was trained on 528 patients, each with 12,042 gene expressions. Then, the autoencoder's weights were used to initialize a supervised deep neural network. The supervised model was trained using a subset of 109 patients with both gene and MR data...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/30079128/transfer-learning-for-diagnosis-of-congenital-abnormalities-of-the-kidney-and-urinary-tract-in-children-based-on-ultrasound-imaging-data
#3
Qiang Zheng, Gregory Tasian, Yong Fan
Classification of ultrasound (US) kidney images for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT) in children is a challenging task. It is desirable to improve existing pattern classification models that are built upon conventional image features. In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children. Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is adopted for transfer learning-based feature extraction from 3-channel feature maps computed from US images, including original images, gradient features, and distanced transform features...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/30079127/non-rigid-image-registration-using-self-supervised-fully-convolutional-networks-without-training-data
#4
Hongming Li, Yong Fan
A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/30079126/integrating-semi-supervised-label-propagation-and-random-forests-for-multi-atlas-based-hippocampus-segmentation
#5
Qiang Zheng, Yong Fan
A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/30079125/brain-age-prediction-based-on-resting-state-functional-connectivity-patterns-using-convolutional-neural-networks
#6
Hongming Li, Theodore D Satterthwaite, Yong Fan
Brain age prediction based on neuroimaging data could help characterize both the typical brain development and neuropsychiatric disorders. Pattern recognition models built upon functional connectivity (FC) measures derived from resting state fMRI (rsfMRI) data have been successfully used to predict the brain age. However, most existing studies focus on coarse-grained FC measures between brain regions or intrinsic connectivity networks (ICNs), which may sacrifice fine-grained FC information of the rsfMRI data...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/30034577/enigma-military-brain-injury-a-coordinated-meta-analysis-of-diffusion-mri-from-multiple-cohorts
#7
Emily L Dennis, Elisabeth A Wilde, Mary R Newsome, Randall S Scheibel, Maya Troyanskaya, Carmen Velez, Benjamin S C Wade, Ann Marie Drennon, Gerald E York, Erin D Bigler, Tracy J Abildskov, Brian A Taylor, Carlos A Jaramillo, Blessen Eapen, Heather Belanger, Vikash Gupta, Rajendra Morey, Courtney Haswell, Harvey S Levin, Sidney R Hinds, William C Walker, Paul M Thompson, David F Tate
Traumatic brain injury (TBI) is a significant cause of morbidity in military Veterans and Service Members. While most individuals recover fully from mild injuries within weeks, some continue to experience symptoms including headaches, disrupted sleep, and other cognitive, behavioral or physical symptoms. Diffusion magnetic resonance imaging (dMRI) shows promise in identifying areas of structural disruption and predicting outcomes. Although some studies suggest widespread structural disruption after brain injury, dMRI studies of military brain injury have yielded mixed results so far, perhaps due to the subtlety of mild injury, individual differences in injury location, severity and mechanism, and comorbidity with other disorders such as post-traumatic stress disorder (PTSD), depression, and substance abuse...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/30023040/multi-task-sparse-screening-for-predicting-future-clinical-scores-using-longitudinal-cortical-thickness-measures
#8
Jie Zhang, Yanshuai Tu, Qingyang Li, Richard J Caselli, Paul M Thompson, Jieping Ye, Yalin Wang
Cortical thickness estimation performed in-vivo via magnetic resonance imaging (MRI) is an effective measure of brain atrophy in preclinical individuals at high risk for Alzheimer's disease (AD). However, the high dimensionality of individual cortical thickness data coupled with small population samples make it challenging to perform cortical thickness feature selection for AD diagnosis and prognosis. Thus far, there are very few methods that can accurately predict future clinical scores using longitudinal cortical thickness measures...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29973975/4d-continuous-medial-representation-by-geodesic-shape-regression
#9
Sungmin Hong, James Fishbaugh, Guido Gerig
Longitudinal shape analysis has shown great potential to model anatomical processes from baseline to follow-up observations. Shape regression estimates a continuous trajectory of time-discrete anatomical shapes to quantify temporal changes. The need for shape alignment and point-to-point correspondences represent limitations of current shape analysis methodologies, and present significant challenges in shape evaluation. We propose a method that estimates a continuous trajectory of continuous medial representations (CM-Rep) from a set of time-discrete observed shapes...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29973974/estimating-shape-correspondence-for-populations-of-objects-with-complex-topology
#10
James Fishbaugh, Laura Pascal, Luke Fischer, Tung Nguyen, Celso Boen, Joao Goncalves, Guido Gerig, Beatriz Paniagua
Statistical shape analysis captures the geometric properties of a given set of shapes, obtained from medical images, by means of statistical methods. Orthognathic surgery is a type of craniofacial surgery that is aimed at correcting severe skeletal deformities in the mandible and maxilla. Methods assuming spherical topology cannot represent the class of anatomical structures exhibiting complex geometries and topologies, including the mandible. In this paper we propose methodology based on non-rigid deformations of 3D geometries to be applied to objects with thin, complex structures...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29899817/scoliosis-screening-and-monitoring-using-self-contained-ultrasound-and-neural-networks
#11
Hastings Greer, Sam Gerber, Marc Niethammer, Roland Kwitt, Matt McCormick, Deepak Chittajallu, Neal Siekierski, Matthew Oetgen, Kevin Cleary, Stephen Aylward
We aim to diagnose scoliosis using a self contained ultrasound device that does not require significant training to operate. The device knows its angle relative to vertical using an embedded inertial measurement unit, and it estimates its angle relative to a vertebrae using a neural network analysis of its ultrasound images. The composition of those angles defines the angle of a vertebrae from vertical. The maximum difference between vertebrae angles collected from a scan of a spine yields the Cobb angle measure that is used to quantify scoliosis severity...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29899816/linear-convolution-model-of-fetal-circulation-for-hemodynamic-responses-to-maternal-hyperoxia-using-in-utero-functional-mri
#12
Wonsang You, Feng Xu, Catherine Limperopoulos
Functional MRI studies have started the hemodynamic responses of the placenta and fetal brain using maternal hyperoxia. While most studies have focused on analyzing the changes in magnitude of fMRI signals, few studies have analyzed the latency and duration of responses to hyperoxia. This paper proposes a linear convolution model of fetal circulation where a chain of responses to maternal hyperoxia are produced in the placenta and fetal brain. Specifically, an impulse response to hyperoxia was modeled as the hemodynamic response function (HRF) which consists of multiple gamma functions...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29887971/efficient-registration-of-pathological-images-a-joint-pca-image-reconstruction-approach
#13
Xu Han, Xiao Yang, Stephen Aylward, Roland Kwitt, Marc Niethammer
Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29276573/a-novel-framework-for-groupwise-registration-of-fmri-images-based-on-common-functional-networks
#14
Yu Zhao, Shu Zhang, Hanbo Chen, Wei Zhang, Lv Jinglei, Xi Jiang, Dinggang Shen, Tianming Liu
Accurate registration plays a critical role in group-wise functional Magnetic Resonance Imaging (fMRI) image analysis, as spatial correspondence among different brain images is a prerequisite for inferring meaningful patterns. However, the problem is challenging and remains open, and more effort should be made to advance the state-of-the-art image registration methods for fMRI images. Inspired by the observation that common functional networks can be reconstructed from fMRI image across individuals, we propose a novel computational framework for simultaneous groupwise fMRI image registration by utilizing those common functional networks as references for spatial alignments...
April 2017: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/29098066/longitudinal-multi-scale-mapping-of-infant-cortical-folding-using-spherical-wavelets
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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