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

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https://read.qxmd.com/read/30713593/global-pdf-based-temporal-non-local-means-filtering-reveals-individual-differences-in-brain-connectivity
#1
Jian Li, Soyoung Choi, Anand A Joshi, Jessica L Wisnowski, Richard M Leahy
Characterizing functional brain connectivity using resting fMRI is challenging due to the relatively small BOLD signal contrast and low SNR. Gaussian filtering tends to undermine the individual differences detected by analysis of BOLD signal by smoothing signals across boundaries of different functional areas. Temporal non-local means (tNLM) filtering denoises fMRI data while preserving spatial structures but the kernel and parameters for tNLM filter need to be chosen carefully in order to achieve optimal results...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30598726/detection-and-tracking-of-migrating-oligodendrocyte-progenitor-cells-from-in-vivo-fluorescence-time-lapse-imaging-data
#2
Yinxue Wang, Maria Ali, Yue Wang, Sarah Kucenas, Guoqiang Yu
In this work, we develop a fully automatic algorithm named "MCDT" (Migrating Cell Detector and Tracker) for the integrated task of migrating cell detection, segmentation and tracking from in vivo fluorescence time-lapse microscopy imaging data. The interest of detecting and tracking migrating cells arouses from the scientific question in understanding the impact of oligodendrocyte progenitor cells (OPCs) migration in vivo , using advanced microscopy imaging techniques. Current practice of OPC mobility analysis relies on manual labeling, suffering from massive human labor, subjective biases, and weak reproducibility...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30559922/time-resolved-displacement-based-registration-of-in-vivo-cdti-cardiomyocyte-orientations
#3
Ilya A Verzhbinsky, Patrick Magrath, Eric Aliotta, Daniel B Ennis, Luigi E Perotti
In vivo cardiac microstructure acquired using cardiac diffusion tensor imaging (cDTI) is a critical component of patient-specific models of cardiac electrophysiology and mechanics. In order to limit bulk motion artifacts and acquisition time, cDTI microstructural data is acquired at a single cardiac phase necessitating registration to the reference configuration on which the patient-specific computational models are based. Herein, we propose a method to register subject-specific microstructural data to an arbitrary cardiac phase using measured cardiac displacements...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30555624/a-label-fusion-aided-convolutional-neural-network-for-isointense-infant-brain-tissue-segmentation
#4
Tengfei Li, Fan Zhou, Ziliang Zhu, Hai Shu, Hongtu Zhu
The extremely low tissue contrast in white matter during an infant's isointense stage (6-8 months) of brain development presents major difficulty when segmenting brain image regions for analysis. We sought to develop a label-fusion-aided deep-learning approach for automatically segmenting isointense infant brain images into white matter, gray matter and cerebrospinal fluid using T1- and T2-weighted magnetic resonance images. A key idea of our approach is to apply the fully convolutional neural network (FCNN) to individual brain regions determined by a traditional registration-based segmentation method instead of training a single model for the whole brain...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30498562/a-computational-method-for-longitudinal-mapping-of-orientation-specific-expansion-of-cortical-surface-area-in-infants
#5
Jing Xia, Caiming Zhang, Fan Wang, Yu Meng, Zhengwang Wu, Li Wang, Weili Lin, Dinggang Shen, Gang Li
The dynamic expansion of the human cortical surface during infancy is largely driven by the increase of surface area in two orthogonal directions: 1) the expansion parallel to the folding orientation (i.e., increasing the lengths of folds) and 2) the expansion perpendicular to the folding orientation (i.e., increasing the depths of folds). The knowledge on this would help us better understand the cortical growth mechanisms and provide important insights into neurodevelopmental disorders, but still remains scarce, due to the lack of dedicated computational methods...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30473744/automatic-renal-segmentation-in-dce-mri-using-convolutional-neural-networks
#6
Marzieh Haghighi, Simon K Warfield, Sila Kurugol
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30464798/infant-brain-development-prediction-with-latent-partial-multi-view-representation-learning
#7
Changqing Zhang, Ehsan Adeli, Zhengwang Wu, Gang Li, Weili Lin, Dinggang Shen
The early postnatal period witnesses rapid and dynamic brain development. Understanding the cognitive development patterns can help identify various disorders at early ages of life and is essential for the health and well-being of children. This inspires us to investigate the relation between cognitive ability and the cerebral cortex by exploiting brain images in a longitudinal study. Specifically, we aim to predict the infant brain development status based on the morphological features of the cerebral cortex...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30450154/estimation-of-shape-and-growth-brain-network-atlases-for-connectomic-brain-mapping-in-developing-infants
#8
Islem Rekik, Gang Li, Weili Lin, Dinggang Shen
In vivo brain connectomics have heavily relied on using functional and diffusion Magnetic Resonance Imaging (MRI) modalities to examine functional and structural relationships between pairs of anatomical regions in the brain. However, research work on brain morphological (i.e., shape-to-shape) connections, which can be derived from T1-w and T2-w MR images, in both typical and atypical development or ageing is very scarce. Furthermore, the brain cannot be only regarded as a static shape, since it is a dynamic complex system that changes at functional, structural and morphological levels...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30450153/emphysema-quantification-on-simulated-x-rays-through-deep-learning-techniques
#9
Mónica Iturrioz Campo, Javier Pascau, Raúl San José Estépar
Emphysema quantification techniques rely on the use of CT scans, but they are rarely used in the diagnosis and management of patients with COPD; X-ray films are the preferred method to do this. However, this diagnosis method is very controversial, as there are not established guidelines to define the disease, sensitivity is low, and quantification cannot be done. We developed a quantification method based on a CNN, capable of predicting the emphysema percentage of a patient based on an X-ray image. We used real CT scans to simulate X-ray films and to calculate emphysema percentage using the LAA%...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30416672/construction-of-spatiotemporal-neonatal-cortical-surface-atlases-using-a-large-scale-dataset
#10
Zhengwang Wu, Gang Li, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen
The cortical surface atlases constructed from a large representative population of neonates are highly needed in the neonatal neuroimaging studies. However, existing neonatal cortical surface atlases are typically constructed from small datasets, e.g., tens of subjects, which are inherently biased and thus are not representative to the neonatal population. In this paper, we construct neonatal cortical surface atlases based on a large-scale dataset with 764 subjects. To better characterize the dynamic cortical development during the first postnatal weeks, instead of constructing just a single atlas, we construct a set of spatiotemporal atlases at each week from 39 to 44 gestational weeks...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30416671/construction-of-spatiotemporal-infant-cortical-surface-atlas-of-rhesus-macaque
#11
Fan Wang, Chunfeng Lian, Jing Xia, Zhengwang Wu, Dingna Duan, Li Wang, Dinggang Shen, Gang Li
As a widely used animal model in MR imaging studies, rhesus macaque helps to better understand both normal and abnormal neural development in the human brain. However, the available adult macaque brain atlases are not well suitable for study of brain development at the early postnatal stage, since this stage undergoes dramatic changes in brain appearances and structures. Building age matched atlases for this critical period is thus highly desirable yet still lacking. In this paper, we construct the first spatiotemporal (4D) cortical surface atlases for rhesus macaques from 2 weeks to 24 months, using 138 longitudinal MRI scans from 32 healthy rhesus monkeys...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30416670/fetal-cortical-parcellation-based-on-growth-patterns
#12
Jing Xia, Caiming Zhang, Fan Wang, Oualid M Benkarim, Gerard Sanroma, Gemma Piella, Miguel A González Balleste, Nadine Hahner, Elisenda Eixarch, Dinggang Shen, Gang Li
Dividing the human cerebral cortex into structurally and functionally distinct regions is important in many neuroimaging studies. Although many parcellations have been created for adults, they are not applicable for fetal studies, due to dramatic differences in brain size, shape and folding between adults and fetuses, as well as dynamic growth of fetal brains. To address this issue, we propose a novel method to divide a population of fetal cortical surfaces into distinct regions based on the dynamic growth patterns of cortical properties, which indicate the underlying changes of microstructures...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30416669/computer-aided-detection-of-pattern-changes-in-longitudinal-adaptive-optics-images-of-the-retinal-pigment-epithelium
#13
Jianfei Liu, HaeWon Jung, Johnny Tam
Retinal pigment epithelium (RPE) defects are indicated in many blinding diseases, but have been difficult to image. Recently, adaptive optics enhanced indocyanine green (AO-ICG) imaging has enabled direct visualization of the RPE mosaic in the living human eye. However, tracking the RPE across longitudinal images on the time scale of months presents with unique challenges, such as visit-to-visit distortion and changes in image quality. We introduce a coarse-to-fine search strategy that identifies paired patterns and measures their changes...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30364770/non-euclidean-convolutional-learning-on-cortical-brain-surfaces
#14
Mahmoud Mostapha, SunHyung Kim, Guorong Wu, Leo Zsembik, Stephen Pizer, Martin Styner
In recent years there have been many studies indicating that multiple cortical features, extracted at each surface vertex, are promising in the detection of various neurodevelopmental and neurodegenerative diseases. However, with limited datasets, it is challenging to train stable classifiers with such high-dimensional surface data. This necessitates a feature reduction that is commonly accomplished via regional volumetric morphometry from standard brain atlases. However, current regional summaries are not specific to the given age or pathology that is studied, which runs the risk of losing relevant information that can be critical in the classification process...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30364524/a-method-for-quantification-of-calponin-expression-in-myoepithelial-cells-in-immunohistochemical-images-of-ductal-carcinoma-in-situ
#15
Elliot Gray, Elizabeth Mitchell, Sonali Jindal, Pepper Schedin, Young Hwan Chang
Ductal carcinoma in situ (DCIS) is breast cancer confined within mammary ducts, surrounded by an intact myoepithelial cell layer that prevents local invasion. A DCIS diagnosis confers increased lifetime risk of developing invasive breast cancer (IBC) and results in surgical excision with radiation, and possibly endocrine- or chemo-therapy. DCIS is known to be over treated, with associated co-morbidities. Biomarkers are needed that delineate patients at low risk of DCIS progression from patients requiring more aggressive treatment...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30344894/cerebral-blood-flow-and-predictors-of-white-matter-lesions-in-adults-with-tetralogy-of-fallot
#16
Yaqiong Chai, Jieshen Chen, Cristina Galarza, Maayke A Sluman, Botian Xu, Chau Q Vu, Edo Richard, Barbara Mulder, Benita Tamrazi, Natasha Lepore, Henri J M M Mutsaerts, John C Wood
Long-term outcomes for Tetralogy of Fallot (TOF) have improved dramatically in recent years, but survivors are still afflicted by cerebral damage. In this paper, we characterized the prevalence and predictors of cerebral silent infarction (SCI) and their relationship to cerebral blood flow (CBF) in 46 adult TOF patients. We calculated both whole brain and regional CBF using 2D arterial spin labeling (ASL) images, and investigated the spatial overlap between voxel-wise CBF values and white matter hyperintensities (WMHs) identified from T2-FLAIR images...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30344893/orchestral-fully-convolutional-networks-for-small-lesion-segmentation-in-brain-mri
#17
Botian Xu, Yaqiong Chai, Cristina M Galarza, Chau Q Vu, Benita Tamrazi, Bilwaj Gaonkar, Luke Macyszyn, Thomas D Coates, Natasha Lepore, John C Wood
White matter (WM) lesion identification and segmentation has proved of clinical importance for diagnosis, treatment and neurological outcomes. Convolutional neural networks (CNN) have demonstrated their success for large lesion load segmentation, but are not sensitive to small deep WM and sub-cortical lesion segmentation. We propose to use multi-scale and supervised fully convolutional networks (FCN) to segment small WM lesions in 22 anemic patients. The multiple scales enable us to identify the small lesions while reducing many false alarms, and the multi-supervised scheme allows a better management of the unbalanced data...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30344892/semi-supervised-learning-for-pelvic-mr-image-segmentation-based-on-multi-task-residual-fully-convolutional-networks
#18
Zishun Feng, Dong Nie, Li Wang, Dinggang Shen
Accurate segmentation of pelvic organs from magnetic resonance (MR) images plays an important role in image-guided radiotherapy. However, it is a challenging task due to inconsistent organ appearances and large shape variations. Fully convolutional network (FCN) has recently achieved state-of-the-art performance in medical image segmentation, but it requires a large amount of labeled data for training, which is usually difficult to obtain in real situation. To address these challenges, we propose a deep learning based semi-supervised learning framework...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30319734/abnormal-hole-detection-in-brain-connectivity-by-kernel-density-of-persistence-diagram-and-hodge-laplacian
#19
Hyekyoung Lee, Moo K Chung, Hyejin Kang, Hongyoon Choi, Yu Kyeong Kim, Dong Soo Lee
Community and rich-club detection are a well-known method to extract functionally specialized subnetwork in brain connectivity analysis. They find densely connected subregions with large modularity or high degree in brain connectivity studies. However, densely connected nodes are not the only representation of network shape. In this study, we propose a new method to extract abnormal holes, which are another representation of network shape. While densely connected component characterizes network's efficiency, abnormal holes characterize inefficiency...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://read.qxmd.com/read/30288208/combining-phenotypic-and-resting-state-fmri-data-for-autism-classification-with-recurrent-neural-networks
#20
Nicha C Dvornek, Pamela Ventola, James S Duncan
Accurate identification of autism spectrum disorder (ASD) from resting-state functional magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the heterogeneity of ASD. Recent work has shown better classification accuracy using a recurrent neural network with rsfMRI time-series as inputs. However, phenotypic features, which are often available and likely carry predictive information, are excluded from the model, and combining such data with rsfMRI into the recurrent neural network is not a straightforward task...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
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