publication . Other literature type . Article . Preprint . 2019

Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks.

Charles Zheng; satra;
Open Access
  • Published: 17 Oct 2019
  • Publisher: Frontiers Media SA
Abstract
Comment: Submitted to Frontiers in Neuroinformatics
Subjects
free text keywords: Biomedical Engineering, Neuroscience (miscellaneous), Computer Science Applications, brain segmentation, deep learning, magnetic resonance imaging, Bayesian neural networks, variational inference, automated quality control, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neuroscience, Original Research, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Funded by
NIH| Nipype: Dataflows for Reproducible Biomedical Research
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01EB020740-02
  • Funding stream: NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING
Communities
Neuroinformatics
65 references, page 1 of 5

Abadi M.Barham P.Chen J.Chen Z.Davis A.Dean J. (2016). Tensorflow: a system for large-scale machine learning, in 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16) (Savannah, GA), 265–283.

Alexander L. M.Escalera J.Ai L.Andreotti C.Febre K.Mangone A.. (2017). An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data 4:170181. 10.1038/sdata.2017.181 29257126 [OpenAIRE] [PubMed] [DOI]

Bellec P.Chu C.Chouinard-Decorte F.Benhajali Y.Margulies D. S.Craddock R. C. (2017). The neuro bureau adhd-200 preprocessed repository. Neuroimage 144, 275–286. 10.1016/j.neuroimage.2016.06.034 27423255 [OpenAIRE] [PubMed] [DOI]

Biswal B. B.Mennes M.Zuo X.-N.Gohel S.Kelly C.Smith S. M.. (2010). Toward discovery science of human brain function. Proc. Natl. Acad. Sci. U.S.A.107, 4734–4739. 10.1073/pnas.0911855107 20176931 [OpenAIRE] [PubMed] [DOI]

Blumenthal J. D.Zijdenbos A.Molloy E.Giedd J. N. (2002). Motion artifact in magnetic resonance imaging: implications for automated analysis. Neuroimage 16, 89–92. 10.1006/nimg.2002.1076 11969320 [OpenAIRE] [PubMed] [DOI]

Blundell C.Cornebise J.Kavukcuoglu K.Wierstra D. (2015). Weight uncertainty in neural networks, in International Conference on Machine Learning (Lille), 1613–1622. [OpenAIRE]

Cardoso M. J.Modat M.Wolz R.Melbourne A.Cash D.Rueckert D.. (2015). Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34, 1976–1988. 10.1109/TMI.2015.2418298 25879909 [PubMed] [DOI]

Der Kiureghian A.Ditlevsen O. (2009). Aleatory or epistemic? Does it matter? Struct. Safety 31, 105–112. 10.1016/j.strusafe.2008.06.020 11349429 [OpenAIRE] [PubMed] [DOI]

Di Martino A.Yan C.-G.Li Q.Denio E.Castellanos F. X.Alaerts K.. (2014). The autism brain imaging data exchange: toward s a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19:659. 10.1038/mp.2013.78 23774715 [OpenAIRE] [PubMed] [DOI]

di Oleggio Castello M. V.Halchenko Y. O.Guntupalli J. S.Gors J. D.Gobbini M. I. (2017). The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception. Sci. Rep. 7 10.1038/s41598-017-12559-1 [DOI]

Dolz J.Desrosiers C.Ayed I. B. (2018). 3d fully convolutional networks for subcortical segmentation in MRI: a large-scale study. NeuroImage 170, 456–470. 10.1016/j.neuroimage.2017.04.039 28450139 [OpenAIRE] [PubMed] [DOI]

Esteban O.Birman D.Schaer M.Koyejo O. O.Poldrack R. A.Gorgolewski K. J. (2017). Mriqc: advancing the automatic prediction of image quality in mri from unseen sites. PLoS ONE 12:e0184661. 10.1371/journal.pone.0184661 28945803 [OpenAIRE] [PubMed] [DOI]

Fedorov A.Damaraju E.Calhoun V.Plis S. (2017a). Almost instant brain atlas segmentation for large-scale studies. arXiv:1711.00457. [OpenAIRE]

Fedorov A.Johnson J.Damaraju E.Ozerin A.Calhoun V.Plis S. (2017b). End-to-end learning of brain tissue segmentation from imperfect labeling, in International Joint Conference on Neural Networks (Anchorage, AK: IEEE), 3785–3792.

Fischl B. (2012). Freesurfer. Neuroimage 62. 10.1016/j.neuroimage.2012.01.021 [OpenAIRE] [DOI]

65 references, page 1 of 5
Abstract
Comment: Submitted to Frontiers in Neuroinformatics
Subjects
free text keywords: Biomedical Engineering, Neuroscience (miscellaneous), Computer Science Applications, brain segmentation, deep learning, magnetic resonance imaging, Bayesian neural networks, variational inference, automated quality control, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neuroscience, Original Research, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Funded by
NIH| Nipype: Dataflows for Reproducible Biomedical Research
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01EB020740-02
  • Funding stream: NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING
Communities
Neuroinformatics
65 references, page 1 of 5

Abadi M.Barham P.Chen J.Chen Z.Davis A.Dean J. (2016). Tensorflow: a system for large-scale machine learning, in 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16) (Savannah, GA), 265–283.

Alexander L. M.Escalera J.Ai L.Andreotti C.Febre K.Mangone A.. (2017). An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data 4:170181. 10.1038/sdata.2017.181 29257126 [OpenAIRE] [PubMed] [DOI]

Bellec P.Chu C.Chouinard-Decorte F.Benhajali Y.Margulies D. S.Craddock R. C. (2017). The neuro bureau adhd-200 preprocessed repository. Neuroimage 144, 275–286. 10.1016/j.neuroimage.2016.06.034 27423255 [OpenAIRE] [PubMed] [DOI]

Biswal B. B.Mennes M.Zuo X.-N.Gohel S.Kelly C.Smith S. M.. (2010). Toward discovery science of human brain function. Proc. Natl. Acad. Sci. U.S.A.107, 4734–4739. 10.1073/pnas.0911855107 20176931 [OpenAIRE] [PubMed] [DOI]

Blumenthal J. D.Zijdenbos A.Molloy E.Giedd J. N. (2002). Motion artifact in magnetic resonance imaging: implications for automated analysis. Neuroimage 16, 89–92. 10.1006/nimg.2002.1076 11969320 [OpenAIRE] [PubMed] [DOI]

Blundell C.Cornebise J.Kavukcuoglu K.Wierstra D. (2015). Weight uncertainty in neural networks, in International Conference on Machine Learning (Lille), 1613–1622. [OpenAIRE]

Cardoso M. J.Modat M.Wolz R.Melbourne A.Cash D.Rueckert D.. (2015). Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34, 1976–1988. 10.1109/TMI.2015.2418298 25879909 [PubMed] [DOI]

Der Kiureghian A.Ditlevsen O. (2009). Aleatory or epistemic? Does it matter? Struct. Safety 31, 105–112. 10.1016/j.strusafe.2008.06.020 11349429 [OpenAIRE] [PubMed] [DOI]

Di Martino A.Yan C.-G.Li Q.Denio E.Castellanos F. X.Alaerts K.. (2014). The autism brain imaging data exchange: toward s a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19:659. 10.1038/mp.2013.78 23774715 [OpenAIRE] [PubMed] [DOI]

di Oleggio Castello M. V.Halchenko Y. O.Guntupalli J. S.Gors J. D.Gobbini M. I. (2017). The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception. Sci. Rep. 7 10.1038/s41598-017-12559-1 [DOI]

Dolz J.Desrosiers C.Ayed I. B. (2018). 3d fully convolutional networks for subcortical segmentation in MRI: a large-scale study. NeuroImage 170, 456–470. 10.1016/j.neuroimage.2017.04.039 28450139 [OpenAIRE] [PubMed] [DOI]

Esteban O.Birman D.Schaer M.Koyejo O. O.Poldrack R. A.Gorgolewski K. J. (2017). Mriqc: advancing the automatic prediction of image quality in mri from unseen sites. PLoS ONE 12:e0184661. 10.1371/journal.pone.0184661 28945803 [OpenAIRE] [PubMed] [DOI]

Fedorov A.Damaraju E.Calhoun V.Plis S. (2017a). Almost instant brain atlas segmentation for large-scale studies. arXiv:1711.00457. [OpenAIRE]

Fedorov A.Johnson J.Damaraju E.Ozerin A.Calhoun V.Plis S. (2017b). End-to-end learning of brain tissue segmentation from imperfect labeling, in International Joint Conference on Neural Networks (Anchorage, AK: IEEE), 3785–3792.

Fischl B. (2012). Freesurfer. Neuroimage 62. 10.1016/j.neuroimage.2012.01.021 [OpenAIRE] [DOI]

65 references, page 1 of 5
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