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A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
[article]
2019
arXiv
pre-print
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We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N=8045 longitudinal brain MRI data of 3249 individuals. ...
In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. ...
Discussion and conclusion We propose a novel hybrid deep learning framework for integrated segmentation and deformable registration in a single fast procedure. ...
arXiv:1908.10221v1
fatcat:vphuywjrhndflj7vopd7szsl74
Front Matter: Volume 9784
2016
Medical Imaging 2016: Image Processing
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in very large white matter fiber sets [9784-9] SESSION 3 COMPUTATIONAL ANATOMY 9784 0B Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation [9784- ...
and multi-atlas labeling [9784-15] 9784 0H Generation and evaluation of an ultra-high-field atlas with applications in DBS planning [9784-16] 9784 0I The bumps on the hippocampus [9784-17] 9784 0J Unsupervised ...
and patient-specific characteristics for prostate segmentation on
3D CT images [9784-78]
9784 28
Patch forest: a hybrid framework of random forest and patch-based segmentation
[9784-79]
9784 29 ...
doi:10.1117/12.2240619
fatcat:kot6cogf4rf6dcjhkzdrr5gahi
Multimodal neuroimaging computing: the workflows, methods, and platforms
2015
Brain Informatics
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Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. ...
We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms. ...
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s40708-015-0020-4
pmid:27747508
pmcid:PMC4737665
fatcat:hr5otfj36ngm7pyvbmvzwlyv4q
Vector Field Streamline Clustering Framework for Brain Fiber Tract Segmentation
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel vector field streamline clustering framework for brain fiber tract segmentations. ...
We also provide qualitative and quantitative evaluations of the IDEC clustering method and QB clustering method. ...
Complementary, clustering-based method group white matter fiber tracts based on fiber tract bodies, focuses on white matter anatomy segmentation with high consistency. ...
arXiv:2011.01795v1
fatcat:geayuf775zemjg7yoqi5kteh3i
Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke
2015
Brain and Behavior
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Many studies focus only on one of these manifestations. A protocol for the differential assessment of all these features is, therefore, needed. ...
Its manifestations on magnetic resonance imaging (MRI) include white matter hyperintensities, lacunes, microbleeds, perivascular spaces, small subcortical infarcts, and brain atrophy. ...
A. Dickie, and S. Wiseman, for providing data for Table 3 , L. Viksne, K. Hoban, and A. K. Heye for their contribution in generating the final ROIs template, X. Wang and M. ...
doi:10.1002/brb3.415
pmid:26807340
pmcid:PMC4714639
fatcat:ccdjxjulljct5a5x2jgxvgha6y
Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review
[article]
2021
arXiv
pre-print
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, segmentation and quantification. ...
We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders ...
CHY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. ...
arXiv:2104.11644v1
fatcat:l3ixpcwu7jb7zcqo2sm5pswmja
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2102.03336v2 fatcat:m4pvasc7cjarfhtu7sewbvusleOther Versions
deep learning approach, such as the convolutional neural networks and autoencoders. ...
Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are ...
The subtle lesions could be manifested on MRI as cortical thickening, blurring of the grey matter-white matter interface and so on [95] . ...
arXiv:2102.03336v3
fatcat:mryusowfbjfjldmx46zcwu6dja
An overview of deep learning in medical imaging focusing on MRI
2018
Zeitschrift für Medizinische Physik
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to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical ...
As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. ...
Our work was financially supported by the Bergen Research Foundation through the project "Computational medical imaging and machine learning -methods, infrastructure and applications". ...
doi:10.1016/j.zemedi.2018.11.002
fatcat:kkimovnwcrhmth7mg6h6cpomjm
Radiogenomics for Precision Medicine With A Big Data Analytics Perspective
2019
IEEE journal of biomedical and health informatics
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challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices. ...
Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the ...
Diffusion tensor imaging (DTI) is a MRI technique that allows the assessment of the microstructural integrity of White Matter (WM) based on fractional anisotropy (FA) and mean diffusivity (MD). ...
doi:10.1109/jbhi.2018.2879381
pmid:30596591
fatcat:rqmjhmdmr5h3rdaody264ogs24
Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI
2016
Frontiers in Neuroanatomy
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through image segmentation, evaluating the effects of genetic and environmental factors on brain development, evaluating sensory stimulation's relationship with functional brain activity and much more ...
This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. ...
ACKNOWLEDGMENTS This article was supported by the National Institute of Health grants R01HD078561 and R03NS091587 to ET. ...
doi:10.3389/fnana.2015.00163
pmid:26834576
pmcid:PMC4720794
fatcat:wctsqbvl6baq5m7dm4snvry5lu
An open, analysis-ready, and quality controlled resource for pediatric brain white-matter research
[article]
2022
bioRxiv
pre-print
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Altogether, this work both delivers a resource for transdiagnostic research in brain connectivity and pediatric mental health and serves as a novel tool for automated QC of large datasets. ...
Data quality plays a key role in the analysis of dMRI, and we provide automated quality control (QC) scores for every scan, as part of the data release. ...
Acknowledgments We would like to thank Anisha Keshavan for useful discussions of community science and webbased quality control and for her work on SwipesForScience. This manuscript was prepared using ...
doi:10.1101/2022.02.24.481303
fatcat:e66jtpwbundkhk3ukylbe5kbve
Derin Öğrenme Araştırma Alanlarının Literatür Taraması
2019
Gazi Mühendislik Bilimleri Dergisi
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Derin öğrenme (Deep Learning-DL), birçok alanda önemli başarılar elde etmiş güçlü bir makine öğrenmesi yöntemidir. ...
In the present day, Deep learning methods have reached better results than humans in object recognition. ...
[177] present a novel automated method based on a cascade of two 3D patch-wise CNN for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. ...
doi:10.30855/gmbd.2019.03.01
fatcat:2sv7dg7elrfqppcjx5otzmb7pi
Diffusion Tensor Imaging-Based Studies at the Group-Level Applied to Animal Models of Neurodegenerative Diseases
2020
Frontiers in Neuroscience
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Thus, the DTI approach provides a promising tool for cross-species cross-sectional and longitudinal investigations of the neurobiological targets and mechanisms of neurodegeneration. ...
Animal models (including disease or knockout models) allow for a variety of experimental manipulations, which are not applicable to humans. ...
ACKNOWLEDGMENTS We would like to thank the Ulm University Center for Translational Imaging MoMAN for its support. ...
doi:10.3389/fnins.2020.00734
pmid:32982659
pmcid:PMC7487414
fatcat:fwb2izfuybfarfdkp4bqqfspum
Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities
2018
Frontiers in Robotics and AI
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Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical ...
synthesize meaningful information from multiple MRI image sets for a diagnosis. ...
A healthy brain has 3 types of tissues: white matter, gray matter, and the cerebrospinal fluid. ...
doi:10.3389/frobt.2018.00120
pmid:33500999
pmcid:PMC7805910
fatcat:jlf2hau7xjdqflhlqunygyxg6i
Brain atrophy and white matter hyperintensities in early Parkinson's disease
2009
Movement Disorders
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The segmentation process resulted in gray matter-, white matter-and CSF-segmented images for each subject. ...
could, for instance, be more use of diffusion MRI of white matter tracts in the brain. ...
doi:10.1002/mds.22754
pmid:19768730
fatcat:khcf2tntcfckrmy53demspcwua
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