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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/30450154/estimation-of-shape-and-growth-brain-network-atlases-for-connectomic-brain-mapping-in-developing-infants
#1
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://www.readbyqxmd.com/read/30450153/emphysema-quantification-on-simulated-x-rays-through-deep-learning-techniques
#2
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://www.readbyqxmd.com/read/30416672/construction-of-spatiotemporal-neonatal-cortical-surface-atlases-using-a-large-scale-dataset
#3
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://www.readbyqxmd.com/read/30416671/construction-of-spatiotemporal-infant-cortical-surface-atlas-of-rhesus-macaque
#4
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://www.readbyqxmd.com/read/30416670/fetal-cortical-parcellation-based-on-growth-patterns
#5
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://www.readbyqxmd.com/read/30416669/computer-aided-detection-of-pattern-changes-in-longitudinal-adaptive-optics-images-of-the-retinal-pigment-epithelium
#6
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://www.readbyqxmd.com/read/30364770/non-euclidean-convolutional-learning-on-cortical-brain-surfaces
#7
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://www.readbyqxmd.com/read/30364524/a-method-for-quantification-of-calponin-expression-in-myoepithelial-cells-in-immunohistochemical-images-of-ductal-carcinoma-in-situ
#8
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://www.readbyqxmd.com/read/30344894/cerebral-blood-flow-and-predictors-of-white-matter-lesions-in-adults-with-tetralogy-of-fallot
#9
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://www.readbyqxmd.com/read/30344893/orchestral-fully-convolutional-networks-for-small-lesion-segmentation-in-brain-mri
#10
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://www.readbyqxmd.com/read/30344892/semi-supervised-learning-for-pelvic-mr-image-segmentation-based-on-multi-task-residual-fully-convolutional-networks
#11
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://www.readbyqxmd.com/read/30319734/abnormal-hole-detection-in-brain-connectivity-by-kernel-density-of-persistence-diagram-and-hodge-laplacian
#12
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://www.readbyqxmd.com/read/30288208/combining-phenotypic-and-resting-state-fmri-data-for-autism-classification-with-recurrent-neural-networks
#13
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
https://www.readbyqxmd.com/read/30271529/joint-exploration-and-mining-of-memory-relevant-brain-anatomic-and-connectomic-patterns-via-a-three-way-association-model
#14
Jingwen Yan, Kefei Liu, Huang Li, Enrico Amico, Shannon L Risacher, Yu-Chien Wu, Shiaofen Fang, Olaf Sporns, Andrew J Saykin, Joaquín Goñi, Li Shen
Early change in memory performance is a key symptom of many brain diseases, but its underlying mechanism remains largely unknown. While structural MRI has been playing an essential role in revealing potentially relevant brain regions, increasing availability of diffusion MRI data (e.g., Human Connectome Project (HCP)) provides excellent opportunities for exploration of their complex coordination. Given the complementary information held in these two imaging modalities, we hypothesize that studying them as a whole, rather than individually, and exploring their association will provide us valuable insights of the memory mechanism...
April 2018: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
https://www.readbyqxmd.com/read/30123414/hippocampus-morphometry-study-on-pathology-confirmed-alzheimer-s-disease-patients-with-surface-multivariate-morphometry-statistics
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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