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

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https://www.readbyqxmd.com/read/28649677/outcome-prediction-for-patient-with-high-grade-gliomas-from-brain-functional-and-structural-networks
#1
Luyan Liu, Han Zhang, Islem Rekik, Xiaobo Chen, Qian Wang, Dinggang Shen
High-grade glioma (HGG) is a lethal cancer, which is characterized by very poor prognosis. To help optimize treatment strategy, accurate preoperative prediction of HGG patient's outcome (i.e., survival time) is of great clinical value. However, there are huge individual variability of HGG, which produces a large variation in survival time, thus making prognostic prediction more challenging. Previous brain imaging-based outcome prediction studies relied only on the imaging intensity inside or slightly around the tumor, while ignoring any information that is located far away from the lesion (i...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28642938/correlation-weighted-sparse-group-representation-for-brain-network-construction-in-mci-classification
#2
Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen
Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28626843/globally-optimal-label-fusion-with-shape-priors
#3
Ipek Oguz, Satyananda Kashyap, Hongzhi Wang, Paul Yushkevich, Milan Sonka
Multi-atlas label fusion methods have gained popularity in a variety of segmentation tasks given their attractive performance. Graph-based segmentation methods are widely used given their global optimality guarantee. We propose a novel approach, GOLF, that combines the strengths of these two approaches. GOLF incorporates shape priors to the label-fusion problem and provides a globally optimal solution even for the multi-label scenario, while also leveraging the highly accurate posterior maps from a multi-atlas label fusion approach...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28626842/automated-segmentation-of-knee-mri-using-hierarchical-classifiers-and-just-enough-interaction-based-learning-data-from-osteoarthritis-initiative
#4
Satyananda Kashyap, Ipek Oguz, Honghai Zhang, Milan Sonka
We present a fully automated learning-based approach for segmenting knee cartilage in presence of osteoarthritis (OA). The algorithm employs a hierarchical set of two random forest classifiers. The first is a neighborhood approximation forest, the output probability map of which is utilized as a feature set for the second random forest (RF) classifier. The output probabilities of the hierarchical approach are used as cost functions in a Layered Optimal Graph Segmentation of Multiple Objects and Surfaces (LOGISMOS)...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28593202/feature-selection-based-on-iterative-canonical-correlation-analysis-for-automatic-diagnosis-of-parkinson-s-disease
#5
Luyan Liu, Qian Wang, Ehsan Adeli, Lichi Zhang, Han Zhang, Dinggang Shen
Parkinson's disease (PD) is a major progressive neurodegenerative disorder. Accurate diagnosis of PD is crucial to control the symptoms appropriately. However, its clinical diagnosis mostly relies on the subjective judgment of physicians and the clinical symptoms that often appear late. Recent neuroimaging techniques, along with machine learning methods, provide alternative solutions for PD screening. In this paper, we propose a novel feature selection technique, based on iterative canonical correlation analysis (ICCA), to investigate the roles of different brain regions in PD through T1-weighted MR images...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28580458/structured-sparse-kernel-learning-for-imaging-genetics-based-alzheimer-s-disease-diagnosis
#6
Jailin Peng, Le An, Xiaofeng Zhu, Yan Jin, Dinggang Shen
A kernel-learning based method is proposed to integrate multimodal imaging and genetic data for Alzheimer's disease (AD) diagnosis. To facilitate structured feature learning in kernel space, we represent each feature with a kernel and then group kernels according to modalities. In view of the highly redundant features within each modality and also the complementary information across modalities, we introduce a novel structured sparsity regularizer for feature selection and fusion, which is different from conventional lasso and group lasso based methods...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28530001/structured-sparse-low-rank-regression-model-for-brain-wide-and-genome-wide-associations
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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 ..
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