F
Florent Ségonne
Researcher at Massachusetts Institute of Technology
Publications - 29
Citations - 19747
Florent Ségonne is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Shape analysis (digital geometry) & Image segmentation. The author has an hindex of 20, co-authored 28 publications receiving 16378 citations. Previous affiliations of Florent Ségonne include École des ponts ParisTech & French Institute for Research in Computer Science and Automation.
Papers
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Journal ArticleDOI
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
Rahul S. Desikan,Florent Ségonne,Bruce Fischl,Bruce Fischl,Brian T. Quinn,Bradford C. Dickerson,Deborah Blacker,Randy L. Buckner,Randy L. Buckner,Anders M. Dale,R. Paul Maguire,Bradley T. Hyman,Marilyn S. Albert,Ronald J. Killiany +13 more
TL;DR: An automated labeling system for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable and may be useful for both morphometric and functional studies of the cerebral cortex.
Journal ArticleDOI
Automatically Parcellating the Human Cerebral Cortex
Bruce Fischl,Andre van der Kouwe,Christophe Destrieux,Eric Halgren,Florent Ségonne,David H. Salat,Evelina Busa,Larry J. Seidman,Jill M. Goldstein,David N. Kennedy,Verne S. Caviness,Nikos Makris,Bruce R. Rosen,Anders M. Dale +13 more
TL;DR: A technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set is presented, comparable in accuracy to manual labeling.
Journal ArticleDOI
Sequence-independent segmentation of magnetic resonance images.
Bruce Fischl,Bruce Fischl,David H. Salat,Andre van der Kouwe,Nikos Makris,Florent Ségonne,Brian T. Quinn,Anders M. Dale,Anders M. Dale +8 more
TL;DR: A set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation.
Journal ArticleDOI
A Hybrid Approach to the Skull Stripping Problem in MRI
Florent Ségonne,Anders M. Dale,Evelina Busa,Maureen Glessner,David H. Salat,Horst K. Hahn,Bruce Fischl +6 more
TL;DR: A novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models is presented, resulting in a robust and automated procedure that outperforms other publicly available skullstripping tools.
Journal ArticleDOI
Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops
TL;DR: The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature.