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Medical Image Computing and Computer-assisted Intervention: MICCAI ...

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https://www.readbyqxmd.com/read/28530001/structured-sparse-low-rank-regression-model-for-brain-wide-and-genome-wide-associations
#1
Xiaofeng Zhu, Heung-Il Suk, Heng Huang, Dinggang Shen
With the advances of neuroimaging techniques and genome sequences understanding, the phenotype and genotype data have been utilized to study the brain diseases (known as imaging genetics). One of the most important topics in image genetics is to discover the genetic basis of phenotypic markers and their associations. In such studies, the linear regression models have been playing an important role by providing interpretable results. However, due to their modeling characteristics, it is limited to effectively utilize inherent information among the phenotypes and genotypes, which are helpful for better understanding their associations...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28417113/temporal-registration-in-in-utero-volumetric-mri-time-series
#2
Ruizhi Liao, Esra A Turk, Miaomiao Zhang, Jie Luo, P Ellen Grant, Elfar Adalsteinsson, Polina Golland
We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28386607/automatic-cystocele-severity-grading-in-ultrasound-by-spatio-temporal-regression
#3
Dong Ni, Xing Ji, Yaozong Gao, Jie-Zhi Cheng, Huifang Wang, Jing Qin, Baiying Lei, Tianfu Wang, Guorong Wu, Dinggang Shen
Cystocele is a common disease in woman. Accurate assessment of cystocele severity is very important for treatment options. The transperineal ultrasound (US) has recently emerged as an alternative tool for cystocele grading. The cystocele severity is usually evaluated with the manual measurement of the maximal descent of the bladder (MDB) relative to the symphysis pubis (SP) during Valsalva maneuver. However, this process is time-consuming and operator-dependent. In this study, we propose an automatic scheme for csystocele grading from transperineal US video...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28386606/progressive-graph-based-transductive-learning-for-multi-modal-classification-of-brain-disorder-disease
#4
Zhengxia Wang, Xiaofeng Zhu, Ehsan Adeli, Yingying Zhu, Chen Zu, Feiping Nie, Dinggang Shen, Guorong Wu
Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis, especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e., extracted from imaging data) in the feature domain, and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However, such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28286884/stability-weighted-matrix-completion-of-incomplete-multi-modal-data-for-disease-diagnosis
#5
Kim-Han Thung, Ehsan Adeli, Pew-Thian Yap, Dinggang Shen
Effective utilization of heterogeneous multi-modal data for Alzheimer's Disease (AD) diagnosis and prognosis has always been hampered by incomplete data. One method to deal with this is low-rank matrix completion (LRMC), which simultaneous imputes missing data features and target values of interest. Although LRMC yields reasonable results, it implicitly weights features from all the modalities equally, ignoring the differences in discriminative power of features from different modalities. In this paper, we propose stability-weighted LRMC (swLRMC), an LRMC improvement that weights features and modalities according to their importance and reliability...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28251192/learning-based-topological-correction-for-infant-cortical-surfaces
#6
Shijie Hao, Gang Li, Li Wang, Yu Meng, Dinggang Shen
Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However, due to rapid growth and ongoing myelination, infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns, thus leading to much more topological errors (holes and handles) in the cortical surfaces derived from tissue segmentation results, in comparison to adult MR images which typically have good tissue contrast...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28229131/discovering-cortical-folding-patterns-in-neonatal-cortical-surfaces-using-large-scale-dataset
#7
Yu Meng, Gang Li, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen
The cortical folding of the human brain is highly complex and variable across individuals. Mining the major patterns of cortical folding from modern large-scale neuroimaging datasets is of great importance in advancing techniques for neuroimaging analysis and understanding the inter-individual variations of cortical folding and its relationship with cognitive function and disorders. As the primary cortical folding is genetically influenced and has been established at term birth, neonates with the minimal exposure to the complicated postnatal environmental influence are the ideal candidates for understanding the major patterns of cortical folding...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28191550/mapping-lifetime-brain-volumetry-with-covariate-adjusted-restricted-cubic-spline-regression-from-cross-sectional-multi-site-mri
#8
Yuankai Huo, Katherine Aboud, Hakmook Kang, Laurie E Cutting, Bennett A Landman
Understanding brain volumetry is essential to understand neurodevelopment and disease. Historically, age-related changes have been studied in detail for specific age ranges (e.g., early childhood, teen, young adults, elderly, etc.) or more sparsely sampled for wider considerations of lifetime aging. Recent advancements in data sharing and robust processing have made available considerable quantities of brain images from normal, healthy volunteers. However, existing analysis approaches have had difficulty addressing (1) complex volumetric developments on the large cohort across the life time (e...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28149968/7t-guided-learning-framework-for-improving-the-segmentation-of-3t-mr-images
#9
Khosro Bahrami, Islem Rekik, Feng Shi, Yaozong Gao, Dinggang Shen
The emerging era of ultra-high-field MRI using 7T MRI scanners dramatically improved sensitivity, image resolution, and tissue contrast when compared to 3T MRI scanners in examining various anatomical structures. The advantages of these high-resolution MR images include higher segmentation accuracy of MRI brain tissues. However, currently, accessibility to 7T MRI scanners remains much more limited than 3T MRI scanners due to technological and economical constraints. Hence, we propose in this work the first learning-based model that improves the segmentation of an input 3T MR image with any conventional segmentation method, through the reconstruction of a higher-quality 7T-like MR image, without actually acquiring an ultra-high-field 7T MRI...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28149967/3d-deep-learning-for-multi-modal-imaging-guided-survival-time-prediction-of-brain-tumor-patients
#10
Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, Dinggang Shen
High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective. In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28149966/new-multi-task-learning-model-to-predict-alzheimer-s-disease-cognitive-assessment
#11
Zhouyuan Huo, Dinggang Shen, Heng Huang
As a neurodegenerative disorder, the Alzheimer's disease (AD) status can be characterized by the progressive impairment of memory and other cognitive functions. Thus, it is an important topic to use neuroimaging measures to predict cognitive performance and track the progression of AD. Many existing cognitive performance prediction methods employ the regression models to associate cognitive scores to neuroimaging measures, but these methods do not take into account the interconnected structures within imaging data and those among cognitive scores...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28149965/prediction-of-memory-impairment-with-mri-data-a-longitudinal-study-of-alzheimer-s-disease
#12
Xiaoqian Wang, Dinggang Shen, Heng Huang
Alzheimer's Disease (AD), a severe type of neurodegenerative disorder with progressive impairment of learning and memory, has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However, traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper, we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28149964/early-diagnosis-of-alzheimer-s-disease-by-joint-feature-selection-and-classification-on-temporally-structured-support-vector-machine
#13
Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Dinggang Shen, Guorong Wu
The diagnosis of Alzheimer's disease (AD) from neuroimaging data at the pre-clinical stage has been intensively investigated because of the immense social and economic cost. In the past decade, computational approaches on longitudinal image sequences have been actively investigated with special attention to Mild Cognitive Impairment (MCI), which is an intermediate stage between normal control (NC) and AD. However, current state-of-the-art diagnosis methods have limited power in clinical practice, due to the excessive requirements such as equal and immoderate number of scans in longitudinal imaging data...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28149963/reveal-consistent-spatial-temporal-patterns-from-dynamic-functional-connectivity-for-autism-spectrum-disorder-identification
#14
Yingying Zhu, Xiaofeng Zhu, Han Zhang, Wei Gao, Dinggang Shen, Guorong Wu
Functional magnetic resonance imaging (fMRI) provides a non-invasive way to investigate brain activity. Recently, convergent evidence shows that the correlations of spontaneous fluctuations between two distinct brain regions dynamically change even in resting state, due to the condition-dependent nature of brain activity. Thus, quantifying the patterns of functional connectivity (FC) in a short time period and changes of FC over time can potentially provide valuable insight into both individual-based diagnosis and group comparison...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28127590/motion-robust-reconstruction-based-on-simultaneous-multi-slice-registration-for-diffusion-weighted-mri-of-moving-subjects
#15
Bahram Marami, Benoit Scherrer, Onur Afacan, Simon K Warfield, Ali Gholipour
Simultaneous multi-slice (SMS) echo-planar imaging has had a huge impact on the acceleration and routine use of diffusion-weighted MRI (DWI) in neuroimaging studies in particular the human connectome project; but also holds the potential to facilitate DWI of moving subjects, as proposed by the new technique developed in this paper. We present a novel registration-based motion tracking technique that takes advantage of the multi-plane coverage of the anatomy by simultaneously acquired slices to enable robust reconstruction of neural microstructure from SMS DWI of moving subjects...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28111644/3d-ultrasonic-needle-tracking-with-a-1-5d-transducer-array-for-guidance-of-fetal-interventions
#16
Wenfeng Xia, Simeon J West, Jean-Martial Mari, Sebastien Ourselin, Anna L David, Adrien E Desjardins
Ultrasound image guidance is widely used in minimally invasive procedures, including fetal surgery. In this context, maintaining visibility of medical devices is a significant challenge. Needles and catheters can readily deviate from the ultrasound imaging plane as they are inserted. When the medical device tips are not visible, they can damage critical structures, with potentially profound consequences including loss of pregnancy. In this study, we performed 3D ultrasonic tracking of a needle using a novel probe with a 1...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28105471/riemannian-statistical-analysis-of-cortical-geometry-with-robustness-to-partial-homology-and-misalignment
#17
Suyash P Awate, Richard M Leahy, Anand A Joshi
Typical studies of the geometry of the cerebral cortical structure focus on either cortical folding or thickness. They rely on spatial normalization, but use cortical descriptors that are sensitive to misregistration arising from the well-known problems of partial homologies between subject brains and local optima in nonlinear registration. In contrast to these approaches, we propose a novel framework for studying the geometry of the entire cortical sheet, subsuming its folding and thickness characteristics...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28090604/xq-nlm-denoising-diffusion-mri-data-via-x-q-space-non-local-patch-matching
#18
Geng Chen, Yafeng Wu, Dinggang Shen, Pew-Thian Yap
Noise is a major issue influencing quantitative analysis in diffusion MRI. The effects of noise can be reduced by repeated acquisitions, but this leads to long acquisition times that can be unrealistic in clinical settings. For this reason, post-acquisition denoising methods have been widely used to improve SNR. Among existing methods, non-local means (NLM) has been shown to produce good image quality with edge preservation. However, currently the application of NLM to diffusion MRI has been mostly focused on the spatial space (i...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28090603/spatial-clockwork-recurrent-neural-network-for-muscle-perimysium-segmentation
#19
Yuanpu Xie, Zizhao Zhang, Manish Sapkota, Lin Yang
Accurate segmentation of perimysium plays an important role in early diagnosis of many muscle diseases because many diseases contain different perimysium inflammation. However, it remains as a challenging task due to the complex appearance of the perymisum morphology and its ambiguity to the background area. The muscle perimysium also exhibits strong structure spanned in the entire tissue, which makes it difficult for current local patch-based methods to capture this long-range context information. In this paper, we propose a novel spatial clockwork recurrent neural network (spatial CW-RNN) to address those issues...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28090602/probabilistic-tractography-for-topographically-organized-connectomes
#20
Dogu Baran Aydogan, Yonggang Shi
While tractography is widely used in brain imaging research, its quantitative validation is highly difficult. Many fiber systems, however, have well-known topographic organization which can even be quantitatively mapped such as the retinotopy of visual pathway. Motivated by this previously untapped anatomical knowledge, we develop a novel tractography method that preserves both topographic and geometric regularity of fiber systems. For topographic preservation, we propose a novel likelihood function that tests the match between parallel curves and fiber orientation distributions...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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