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Machine Learning in Medical Imaging

Guannan Li, Mingxia Liu, Quansen Sun, Dinggang Shen, Li Wang
Currently there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavior observations at three or four years old. Since intervention efforts may miss a critical developmental window after 2 years old, it is significant to identify imaging-based biomarkers for early diagnosis of ASD. Although some methods using magnetic resonance imaging (MRI) for brain disease prediction have been proposed in the last decade, few of them were developed for predicting ASD in early age...
September 2018: Machine Learning in Medical Imaging
Jiawei Chen, Han Zhang, Dong Nie, Li Wang, Gang Li, Weili Lin, Dinggang Shen
The human cerebellum has been recognized as a key brain structure for motor control and cognitive function regulation. Investigation of brain functional development in the early life has recently been focusing on both cerebral and cerebellar development. Accurate segmentation of the infant cerebellum into different tissues is among the most important steps for quantitative development studies. However, this is extremely challenging due to the weak tissue contrast, extremely folded structures, and severe partial volume effect...
September 2018: Machine Learning in Medical Imaging
Baris U Oguz, Russell T Shinohara, Paul A Yushkevich, Ipek Oguz
Random forests (RF) have long been a widely popular method in medical image analysis. Meanwhile, the closely related gradient boosted trees (GBT) have not become a mainstream tool in medical imaging despite their attractive performance, perhaps due to their computational cost. In this paper, we leverage the recent availability of an efficient open-source GBT implementation to illustrate the GBT method in a corrective learning framework, in application to the segmentation of the caudate nucleus, putamen and hippocampus...
September 2017: Machine Learning in Medical Imaging
Yu Zhang, Han Zhang, Xiaobo Chen, Mingxia Liu, Xiaofeng Zhu, Dinggang Shen
Sparse representation-based brain network modeling, although popular, often results in relatively large inter-subject variability in network structures. This inevitably makes it difficult for inter-subject comparison, thus eventually deteriorating the generalization capability of personalized disease diagnosis. Accordingly, group sparse representation has been proposed to alleviate such limitation by jointly estimating connectivity weights for all subjects. However, the constructed brain networks based on this method often fail in providing satisfactory separability between the subjects from different groups (e...
September 2017: Machine Learning in Medical Imaging
Dmitry Petrov, Boris A Gutman, Shih-Hua Julie Yu, Theo G M van Erp, Jessica A Turner, Lianne Schmaal, Dick Veltman, Lei Wang, Kathryn Alpert, Dmitry Isaev, Artemis Zavaliangos-Petropulu, Christopher R K Ching, Vince Calhoun, David Glahn, Theodore D Satterthwaite, Ole Andreas Andreasen, Stefan Borgwardt, Fleur Howells, Nynke Groenewold, Aristotle Voineskos, Joaquim Radua, Steven G Potkin, Benedicto Crespo-Facorro, Diana Tordesillas-Gutiérrez, Li Shen, Irina Lebedeva, Gianfranco Spalletta, Gary Donohoe, Peter Kochunov, Pedro G P Rosa, Anthony James, Udo Dannlowski, Bernhard T Baune, André Aleman, Ian H Gotlib, Henrik Walter, Martin Walter, Jair C Soares, Stefan Ehrlich, Ruben C Gur, N Trung Doan, Ingrid Agartz, Lars T Westlye, Fabienne Harrisberger, Anita Riecher-Rössler, Anne Uhlmann, Dan J Stein, Erin W Dickie, Edith Pomarol-Clotet, Paola Fuentes-Claramonte, Erick Jorge Canales-Rodríguez, Raymond Salvador, Alexander J Huang, Roberto Roiz-Santiañez, Shan Cong, Alexander Tomyshev, Fabrizio Piras, Daniela Vecchio, Nerisa Banaj, Valentina Ciullo, Elliot Hong, Geraldo Busatto, Marcus V Zanetti, Mauricio H Serpa, Simon Cervenka, Sinead Kelly, Dominik Grotegerd, Matthew D Sacchet, Ilya M Veer, Meng Li, Mon-Ju Wu, Benson Irungu, Esther Walton, Paul M Thompson
As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality...
September 2017: Machine Learning in Medical Imaging
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
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
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
Yang Li, Hao Yang, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen
Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference methods are based on correlation analysis and consider the datum points individually, revealing limited information of the neuron interactions and ignoring the relations amongst the derivatives of the data. Hence, we proposed a novel ultra group-constrained sparse linear regression model for effective connectivity inference...
2017: Machine Learning in Medical Imaging
Yang Li, Jingyu Liu, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen
Functional connectivity network derived from resting-state fMRI data has been found as effective biomarkers for identifying patients with mild cognitive impairment from healthy elderly. However, the ordinary functional connectivity network is essentially a low-order network with the assumption that the brain is static during the entire scanning period, ignoring the temporal variations among correlations derived from brain region pairs. To overcome this weakness, we proposed a new type of high-order network to more accurately describe the relationship of temporal variations among brain regions...
2017: Machine Learning in Medical Imaging
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
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
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
Chen Zu, Yue Gao, Brent Munsell, Minjeong Kim, Ziwen Peng, Yingying Zhu, Wei Gao, Daoqiang Zhang, Dinggang Shen, Guorong Wu
The functional connectome has gained increased attention in the neuroscience community. In general, most network connectivity models are based on correlations between discrete-time series signals that only connect two different brain regions. However, these bivariate region-to-region models do not involve three or more brain regions that form a subnetwork. Here we propose a learning-based method to explore subnetwork biomarkers that are significantly distinguishable between two clinical cohorts. Learning on hypergraph is employed in our work...
October 2016: Machine Learning in Medical Imaging
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
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
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
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
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
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
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