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Machine Learning in Medical Imaging

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https://www.readbyqxmd.com/read/29376149/feature-learning-and-fusion-of-multimodality-neuroimaging-and-genetic-data-for-multi-status-dementia-diagnosis
#1
Tao Zhou, Kim-Han Thung, Xiaofeng Zhu, Dinggang Shen
In this paper, we aim to maximally utilize multimodality neuroimaging and genetic data to predict Alzheimer's disease (AD) and its prodromal status, i.e., a multi-status dementia diagnosis problem. Multimodality neuroimaging data such as MRI and PET provide valuable insights to abnormalities, and genetic data such as Single Nucleotide Polymorphism (SNP) provide information about a patient's AD risk factors. When used in conjunction, AD diagnosis may be improved. However, these data are heterogeneous (e.g., having different data distributions), and have different number of samples (e...
September 2017: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/29270583/structural-connectivity-guided-sparse-effective-connectivity-for-mci-identification
#2
Yang Li, Jingyu Liu, Meilin Luo, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen
Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies...
September 2017: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/29104967/identifying-autism-from-resting-state-fmri-using-long-short-term-memory-networks
#3
Nicha C Dvornek, Pamela Ventola, Kevin A Pelphrey, James S Duncan
Functional magnetic resonance imaging (fMRI) has helped characterize the pathophysiology of autism spectrum disorders (ASD) and carries promise for producing objective biomarkers for ASD. Recent work has focused on deriving ASD biomarkers from resting-state functional connectivity measures. However, current efforts that have identified ASD with high accuracy were limited to homogeneous, small datasets, while classification results for heterogeneous, multi-site data have shown much lower accuracy. In this paper, we propose the use of recurrent neural networks with long short-term memory (LSTMs) for classification of individuals with ASD and typical controls directly from the resting-state fMRI time-series...
September 2017: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/29457153/sparse-multi-view-task-centralized-learning-for-asd-diagnosis
#4
Jun Wang, Qian Wang, Shitong Wang, Dinggang Shen
It is challenging to derive early diagnosis from neuroimaging data for autism spectrum disorder (ASD). In this work, we propose a novel sparse multi-view task-centralized (Sparse-MVTC) classification method for computer-assisted diagnosis of ASD. In particular, since ASD is known to be age- and sex-related, we partition all subjects into different groups of age/sex, each of which can be treated as a classification task to learn. Meanwhile, we extract multi-view features from functional magnetic resonance imaging to describe the brain connectivity of each subject...
2017: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/29417097/segmentation-of-craniomaxillofacial-bony-structures-from-mri-with-a-3d-deep-learning-based-cascade-framework
#5
Dong Nie, Li Wang, Roger Trullo, Jianfu Li, Peng Yuan, James Xia, Dinggang Shen
Computed tomography (CT) is commonly used as a diagnostic and treatment planning imaging modality in craniomaxillofacial (CMF) surgery to correct patient's bony defects. A major disadvantage of CT is that it emits harmful ionizing radiation to patients during the exam. Magnetic resonance imaging (MRI) is considered to be much safer and noninvasive, and often used to study CMF soft tissues (e.g., temporomandibular joint and brain). However, it is extremely difficult to accurately segment CMF bony structures from MRI since both bone and air appear to be black in MRI, along with low signal-to-noise ratio and partial volume effect...
2017: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/29276808/efficient-groupwise-registration-for-brain-mri-by-fast-initialization
#6
Pei Dong, Xiaohuan Cao, Jun Zhang, Minjeong Kim, Guorong Wu, Dinggang Shen
Groupwise image registration provides an unbiased registration solution upon a population of images, which can facilitate the subsequent population analysis. However, it is generally computationally expensive for performing groupwise registration on a large set of images. To alleviate this issue, we propose to utilize a fast initialization technique for speeding up the groupwise registration. Our main idea is to generate a set of simulated brain MRI samples with known deformations to their group center. This can be achieved in the training stage by two steps...
2017: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28603792/regression-guided-deformable-models-for-segmentation-of-multiple-brain-rois
#7
Zhengwang Wu, Sang Hyun Park, Yanrong Guo, Yaozong Gao, Dinggang Shen
This paper proposes a novel method of using regression-guided deformable models for brain regions of interest (ROIs) segmentation. Different from conventional deformable segmentation, which often deforms shape model locally and thus sensitive to initialization, we propose to learn a regressor to explicitly guide the shape deformation, thus eventually improves the performance of ROI segmentation. The regressor is learned via two steps, (1) a joint classification and regression random forest (CRRF) and (2) an auto-context model...
October 2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28603791/automatic-hippocampal-subfield-segmentation-from-3t-multi-modality-images
#8
Zhengwang Wu, Yaozong Gao, Feng Shi, Valerie Jewells, Dinggang Shen
Hippocampal subfields play important and divergent roles in both memory formation and early diagnosis of many neurological diseases, but automatic subfield segmentation is less explored due to its small size and poor image contrast. In this paper, we propose an automatic learning-based hippocampal subfields segmentation framework using multi-modality 3TMR images, including T1 MRI and resting-state fMRI (rs-fMRI). To do this, we first acquire both 3T and 7T T1 MRIs for each training subject, and then the 7T T1 MRI are linearly registered onto the 3T T1 MRI...
October 2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28603790/dual-layer-groupwise-registration-for-consistent-labeling-of-longitudinal-brain-images
#9
Minjeong Kim, Guorong Wu, Isrem Rekik, Dinggang Shen
The growing collection of longitudinal images for brain disease diagnosis necessitates the development of advanced longitudinal registration and anatomical labeling methods that can respect temporal consistency between images. However, the characteristics of such longitudinal images and how they lodge into the image manifold are often neglected in existing labeling methods. Indeed, most of them independently align atlases to each target time-point image for propagating the pre-defined atlas labels to the subject domain...
October 2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28603789/segmentation-of-perivascular-spaces-using-vascular-features-and-structured-random-forest-from-7t-mr-image
#10
Jun Zhang, Yaozong Gao, Sang Hyun Park, Xiaopeng Zong, Weili Lin, Dinggang Shen
Quantitative analysis of perivascular spaces (PVSs) is important to reveal the correlations between cerebrovascular lesions and neurodegenerative diseases. In this study, we propose a learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into PVS and background. In addition, we also propose a novel entropy-based sampling strategy to extract informative samples in the background for training the classification model...
October 2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28232959/unsupervised-discovery-of-emphysema-subtypes-in-a-large-clinical-cohort
#11
Polina Binder, Nematollah K Batmanghelich, Raul San Jose Estepar, Polina Golland
Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disorder (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with disease subtypes. It has been shown that the disease subtypes and textures are linked to physiological indicators and prognosis, although neither is well characterized clinically. Most previous computational approaches to modeling emphysema imaging data have focused on supervised classification of lung textures in patches of CT scans...
October 2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28090600/learning-based-3t-brain-mri-segmentation-with-guidance-from-7t-mri-labeling
#12
Renping Yu, Minghui Deng, Pew-Thian Yap, Zhihui Wei, Li Wang, Dinggang Shen
Brain magnetic resonance image segmentation is one of the most important tasks in medical image analysis and has considerable importance to the effective use of medical imagery in clinical and surgical setting. In particular, the tissue segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain measurement and disease diagnosis. A variety of studies have shown that the learning-based techniques are efficient and effective in brain tissue segmentation. However, the learning-based segmentation methods depend largely on the availability of good training labels...
October 2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28959800/fast-neuroimaging-based-retrieval-for-alzheimer-s-disease-analysis
#13
Xiaofeng Zhu, Kim-Han Thung, Jun Zhang, Dinggang She
This paper proposes a framework of fast neuroimaging-based retrieval and AD analysis, by three key steps: (1) landmark detection, which efficiently extracts landmark-based neuroimaging features without the need of nonlinear registration in testing stage; (2) landmark selection, which removes redundant/noisy landmarks via proposing a feature selection method that considers structural information among landmarks; and (3) hashing, which converts high-dimensional features of subjects into binary codes, for efficiently conducting approximate nearest neighbor search and diagnosis of AD...
2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28956028/joint-discriminative-and-representative-feature-selection-for-alzheimer-s-disease-diagnosis
#14
Xiaofeng Zhu, Heung-Il Suk, Kim-Han Thung, Yingying Zhu, Guorong Wu, Dinggang Shen
Neuroimaging data have been widely used to derive possible biomarkers for Alzheimer's Disease (AD) diagnosis. As only certain brain regions are related to AD progression, many feature selection methods have been proposed to identify informative features (i.e., brain regions) to build an accurate prediction model. These methods mostly only focus on the feature-target relationship to select features which are discriminative to the targets (e.g., diagnosis labels). However, since the brain regions are anatomically and functionally connected, there could be useful intrinsic relationships among features...
2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/28944345/functional-connectivity-network-fusion-with-dynamic-thresholding-for-mci-diagnosis
#15
Xi Yang, Yan Jin, Xiaobo Chen, Han Zhang, Gang Li, Dinggang Shen
The resting-state functional MRI (rs-fMRI) has been demonstrated as a valuable neuroimaging tool to identify mild cognitive impairment (MCI) patients. Previous studies showed network breakdown in MCI patients with thresholded rs-fMRI connectivity networks. Recently, machine learning techniques have assisted MCI diagnosis by integrating information from multiple networks constructed with a range of thresholds. However, due to the difficulty of searching optimal thresholds, they are often predetermined and uniformly applied to the entire network...
2016: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/26913296/hierarchical-multi-modal-image-registration-by-learning-common-feature-representations
#16
Hongkun Ge, Guorong Wu, Li Wang, Yaozong Gao, Dinggang Shen
Mutual information (MI) has been widely used for registering images with different modalities. Since most inter-modality registration methods simply estimate deformations in a local scale, but optimizing MI from the entire image, the estimated deformations for certain structures could be dominated by the surrounding unrelated structures. Also, since there often exist multiple structures in each image, the intensity correlation between two images could be complex and highly nonlinear, which makes global MI unable to precisely guide local image deformation...
October 5, 2015: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/27570846/longitudinal-patch-based-segmentation-of-multiple-sclerosis-white-matter-lesions
#17
Snehashis Roy, Aaron Carass, Jerry L Prince, Dzung L Pham
Segmenting T2-hyperintense white matter lesions from longitudinal MR images is essential in understanding progression of multiple sclerosis. Most lesion segmentation techniques find lesions independently at each time point, even though there are different noise and image contrast variations at each point in the time series. In this paper, we present a patch based 4D lesion segmentation method that takes advantage of the temporal component of longitudinal data. For each subject with multiple time-points, 4D patches are constructed from the T 1-w and FLAIR scans of all time-points...
October 2015: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/27088137/inherent-structure-guided-multi-view-learning-for-alzheimer-s-disease-and-mild-cognitive-impairment-classification
#18
Mingxia Liu, Daoqiang Zhang, Dinggang Shen
Multi-atlas based morphometric pattern analysis has been recently proposed for the automatic diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI), where multi-view feature representations for subjects are generated by using multiple atlases. However, existing multi-atlas based methods usually assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while the underlying distribution of data is actually a prior unknown...
October 2015: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/26900608/multi-view-classification-for-identification-of-alzheimer-s-disease
#19
Xiaofeng Zhu, Heung-Il Suk, Yonghua Zhu, Kim-Han Thung, Guorong Wu, Dinggang Shen
In this paper, we propose a multi-view learning method using Magnetic Resonance Imaging (MRI) data for Alzheimer's Disease (AD) diagnosis. Specifically, we extract both Region-Of-Interest (ROI) features and Histograms of Oriented Gradient (HOG) features from each MRI image, and then propose mapping HOG features onto the space of ROI features to make them comparable and to impose high intra-class similarity with low inter-class similarity. Finally, both mapped HOG features and original ROI features are input to the support vector machine for AD diagnosis...
2015: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/26900607/identification-of-infants-at-risk-for-autism-using-multi-parameter-hierarchical-white-matter-connectomes
#20
Yan Jin, Chong-Yaw Wee, Feng Shi, Kim-Han Thung, Pew-Thian Yap, Dinggang Shen
Autism spectrum disorder (ASD) is a variety of developmental disorders that cause life-long communication and social deficits. However, ASD could only be diagnosed at children as early as 2 years of age, while early signs may emerge within the first year. White matter (WM) connectivity abnormalities have been documented in the first year of lives of ASD subjects. We introduce a novel multi-kernel support vector machine (SVM) framework to identify infants at high-risk for ASD at 6 months old, by utilizing the diffusion parameters derived from a hierarchical set of WM connectomes...
2015: Machine Learning in Medical Imaging
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