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A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes [article]

Bo Li, Wiro Niessen, Stefan Klein, Marius de Groot, Arfan Ikram, Meike Vernooij, Esther Bron
2019 arXiv   pre-print
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  
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

Sidong Liu, Weidong Cai, Siqi Liu, Fan Zhang, Michael Fulham, Dagan Feng, Sonia Pujol, Ron Kikinis
2015 Brain Informatics  
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]

Chaoqing Xu, Guodao Sun, Ronghua Liang, Xiufang Xu
2020 arXiv   pre-print
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

Maria del C. Valdés Hernández, Paul A. Armitage, Michael J. Thrippleton, Francesca Chappell, Elaine Sandeman, Susana Muñoz Maniega, Kirsten Shuler, Joanna M. Wardlaw
2015 Brain and Behavior  
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]

Fan Zhang, Alessandro Daducci, Yong He, Simona Schiavi, Caio Seguin, Robert Smith, Chun-Hung Yeh, Tengda Zhao, Lauren J. O'Donnell
2021 arXiv   pre-print
, 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]

Jie Yuan, Xuming Ran, Keyin Liu, Chen Yao, Yi Yao, Haiyan Wu, Quanying Liu
2021 arXiv   pre-print
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

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
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

Andreas S. Panayides, Marios Pattichis, Stephanos Leandrou, Costas Pitris, Anastasia Constantinidou, Constantinos S. Pattichis
2019 IEEE journal of biomedical and health informatics  
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

Jacob Levman, Emi Takahashi
2016 Frontiers in Neuroanatomy  
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]

Adam Richie-Halford, Matthew Cieslak, Lei Ai, Sendy Caffarra, Sydney Covitz, Alexandre R. Franco, Iliana I. Karipidis, John Kruper, Michael Milham, Bárbara Avelar-Pereira, Ethan Roy, Valerie J. Sydnor (+4 others)
2022 bioRxiv   pre-print
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

M. Mutlu Yapıcı, Adem Tekerek, Nurettin Topaloğlu
2019 Gazi Mühendislik Bilimleri Dergisi  
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

Hans-Peter Müller, Francesco Roselli, Volker Rasche, Jan Kassubek
2020 Frontiers in Neuroscience  
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

Martin Cenek, Masa Hu, Gerald York, Spencer Dahl
2018 Frontiers in Robotics and AI  
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

Turi O. Dalaker, Jan P. Larsen, Niels Bergsland, Mona K. Beyer, Guido Alves, Michael G. Dwyer, Ole-Bjorn Tysnes, Ralph H.B. Benedict, Arpad Kelemen, Kolbjorn Bronnick, Robert Zivadinov
2009 Movement Disorders  
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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