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We present a deep learning strategy that enables, for the first time, contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI scans, without requiring additional training or fine-tuning for new modalities.
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Adrian Dalca, Evan Yu, Polina Golland, Bruce Fischl, Mert Sabuncu, and Juan Eugenio Iglesias · 1904
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Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder
Woo Tae, Sam Kim, Kang Lee, Eui Nam, and Keun Kim · 1920
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Adaptive segmentation of MRI data
W.M. Wells, W. Grimson, R. Kikinis, and F. Jolesz · 1996
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Multimodality image registration by maximization of mutual information
F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens · 1997
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Automated model-based tissue classification of MR images of the brain
K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens · 1999
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Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
Y. Zhang, M. Brady, and S. Smith · 2001
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Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
Bruce Fischl, David Salat, Evelina Busa, Marilyn Albert, Megan Dieterich, Christian Haselgrove, Andre van der Kouwe, Ron Killiany, David Kennedy, Shuna Klaveness, Albert Montillo, Nikos Makris, Bruce Rosen, and Anders Dale · 2002
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Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later
Cleve. Moler and Charles. Van Loan · 2003
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Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains
Torsten Rohlfing, Robert Brandt, Randolf Menzel, and Calvin Maurer · 2003
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Unified segmentation
John Ashburner and Karl Friston · 2005
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A log-Euclidean framework for statistics on diffeomorphisms
Vincent Arsigny, Olivier Commowick, Xavier Pennec, and Nicholas Ayache · 2006
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Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults
Daniel Marcus, Tracy Wang, Jamie Parker, John Csernansky, John Morris, and Randy Buckner · 2007
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A Generative Model for Image Segmentation Based on Label Fusion
Mert Sabuncu, Thomas Yeo, Koen Van Leemput, Bruce Fischl, and Polina Golland · 2010
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The Parkinson Progression Marker Initiative (PPMI)
Kenneth Marek, Danna Jennings, Shirley Lasch, Andrew Siderowf, Caroline Tanner, Tanya Simuni, Chris Coffey, Karl Kieburtz, Emily Flagg, Sohini Chowdhury, Werner Poewe, Brit Mollenhauer, Todd Sherer, Mark Frasier, Claire Meunier, Alice Rudolph, Cindy Casaceli, John Seibyl, Susan Mendick, Norbert Schuff, Ying Zhang, Arthur Toga, Karen Crawford, Alison Ansbach, Pasquale de Blasio, Michele Piovella, John Trojanowski, Les Shaw, Andrew Singleton, Keith Hawkins, Jamie Eberling, David Russell, Laura Leary, Stewart Factor, Barbara Sommerfeld, Penelope Hogarth, Emily Pighetti, Karen Williams, David Standaert, Stephanie Guthrie, Robert Hauser, Holly Delgado, Joseph Jankovic, Christine Hunter, Matthew Stern, Baochan Tran, Jim Leverenz, Marne Baca, Sam Frank, Cathi Ann Thomas, Irene Richard, Cheryl Deeley, Linda Rees, Fabienne Sprenger, Elisabeth Lang, Holly Shill, Sanja Obradov, Hubert Fernandez, Adrienna Winters, Daniela Berg, Katharina Gauss, Douglas Galasko, Deborah Fontaine, Zoltan Mari, Melissa Gerstenhaber, David Brooks, Sophie Malloy, Paolo Barone, Katia Longo, Tom Comery, Bernard Ravina, Igor Grachev, Kim Gallagher, Michelle Collins, Katherine L. Widnell, Suzanne Ostrowizki, Paulo Fontoura, F. Hoffmann La-Roche, Tony Ho, Johan Luthman, Marcel van der Brug, Alastair D. Reith, and Peggy Taylor · 2011
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A Bayesian model of shape and appearance for subcortical brain segmentation
Brian Patenaude, Stephen Smith, David Kennedy, and Mark Jenkinson · 2011
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SPM: a history
John Ashburner · 2012
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The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience
The ADHD-200 Consortium · 2012
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FreeSurfer
Bruce Fischl · 2012
Cited alongside, same era.
The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia
Randy Gollub, Jody Shoemaker, Margaret King, Tonya White, Stefan Ehrlich, Scott Sponheim, Vincent Clark, Jessica Turner, Bryon Mueller, Vince Magnotta, Daniel O’Leary, Beng Ho, Stefan Brauns, Dara Manoach, Larry Seidman, Juan Bustillo, John Lauriello, Jeremy Bockholt, Kelvin Lim, Bruce Rosen, Charles Schulz, Vince Calhoun, and Nancy Andreasen · 2013
Cited alongside, same era.
Is synthesizing MRI contrast useful for inter-modality analysis?
Juan Eugenio Iglesias, Ender Konukoglu, Darko Zikic, Ben Glocker, Koen Van Leemput, and Bruce Fischl · 2013
Cited alongside, same era.
The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism
Adriana Di Martino, Chao-Gan Yan, Qingyang Li, Erin Denio, Francisco Castellanos, Kaat Alaerts, Jeffrey Anderson, Michal Assaf, Susan Bookheimer, Mirella Dapretto, Ben Deen, Sonja Delmonte, Ilan Dinstein, Birgit Ertl-Wagner, Damien Fair, Louise Gallagher, Daniel Kennedy, Christopher Keown, Christian Keysers, Janet Lainhart, Catherine Lord, Beatriz Luna, Vinod Menon, Nancy Minshew, Christopher Monk, Sophia Mueller, Ralph-Axel Müller, Mary Beth Nebel, Joel Nigg, Kirsten O’Hearn, Kevin Pelphrey, Scott Peltier, Jeffrey Rudie, Stefan Sunaert, Marc Thioux, Michael Tyszka, Lucina Uddin, Judith Verhoeven, Nicole Wenderoth, Jillian Wiggins, Stewart Mostofsky, and Michael Milham · 2014
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
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Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling
Oula Puonti, Juan Eugenio Iglesias, and Koen Van Leemput · 2016
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3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
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Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions
Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel Rubin, and Bradley Erickson · 2017
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Multimodal MR Synthesis via Modality-Invariant Latent Representation
Agisilaos Chartsias, Thomas Joyce, Mario Giuffrida, and Sotirios Tsaftaris · 2017
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N3 Bias Field Correction Explained as a Bayesian Modeling Method
Christian Larsen, J. Eugenio Iglesias, and Koen Van Leemput · 2014
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Keras (https://github.com/fchollet/keras)
François Chollet · 2015
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Harvard Aging Brain Study: dataset and accessibility
Alexander Dagley, Molly LaPoint, Willem Huijbers, Trey Hedden, Donald McLaren, Jasmeer Chatwal, Kathryn Papp, Rebecca Amariglio, Deborah Blacker, Dorene Rentz, Keith Johnson, Reisa Sperling, and Aaron Schultz · 2015
Cited alongside, same era.
Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures
Avram Holmes, Marisa Hollinshead, Timothy O’Keefe, Victor Petrov, Gabriele Fariello, Lawrence Wald, Bruce Fischl, Bruce Rosen, Ross Mair, Joshua Roffman, Jordan Smoller, and Randy Buckner · 2015
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Multi-atlas segmentation of biomedical images: a survey
Juan Eugenio Iglesias and Mert R Sabuncu · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe and Christian Szegedy · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
Bob de Vos, Floris Berendsen, Max Viergever, Marius Staring, and Ivana Išgum · 2017
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Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
Adrian V. Dalca, John Guttag, and Mert R. Sabuncu · 2018
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Pulse Sequence Resilient Fast Brain Segmentation
Amod Jog and Bruce Fischl · 2018
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A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
Neerav Karani, Krishna Chaitanya, Christian Baumgartner, and Ender Konukoglu · 2018
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VoxelMorph: A Learning Framework for Deformable Medical Image Registration
Guha Balakrishnan, Amy Zhao, Mert Sabuncu, John Guttag, and Adrian Dalca · 2019
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Semi-supervised and Task-Driven Data Augmentation
Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner, Anton Becker, Olivio Donati, and Ender Konukoglu · 2019
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Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces
Adrian V. Dalca, Guha Balakrishnan, John Guttag, and Mert Sabuncu · 2019
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SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara Moyo, Michael Savona, Richard Abramson, and Bennett Landman · 2019
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Learning a Probabilistic Model for Diffeomorphic Registration
Julian Krebs, Hervé Delingette, Boris Mailhé, Nicholas Ayache, and Tommaso Mansi · 2019
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QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger, Alzheimer’s Disease Neuroimaging Initiative, and others · 2019
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Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation
Amy Zhao, Guha Balakrishnan, Fredo Durand, John Guttag, and Adrian Dalca · 2019
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