J
James C. Gee
Researcher at University of Pennsylvania
Publications - 447
Citations - 40540
James C. Gee is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Image registration & Diffusion MRI. The author has an hindex of 69, co-authored 431 publications receiving 33177 citations. Previous affiliations of James C. Gee include University of Rennes 1 & University of Wisconsin-Madison.
Papers
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Journal ArticleDOI
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
Paul A. Yushkevich,Joseph Piven,Heather C. Hazlett,Rachel Gimpel Smith,Sean Ho,James C. Gee,Guido Gerig +6 more
TL;DR: The methods and software engineering philosophy behind this new tool, ITK-SNAP, are described and the results of validation experiments performed in the context of an ongoing child autism neuroimaging study are provided, finding that SNAP is a highly reliable and efficient alternative to manual tracing.
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Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.
TL;DR: This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
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N4ITK: Improved N3 Bias Correction
Nicholas J. Tustison,Brian B. Avants,Philip A. Cook,Yuanjie Zheng,A Egan,Paul A. Yushkevich,James C. Gee +6 more
TL;DR: A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction with the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field Correction over the original N3 algorithm.
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A reproducible evaluation of ANTs similarity metric performance in brain image registration.
TL;DR: This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling, and to quantify the similarity of templates derived from different subgroups.
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Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.
Arno Klein,Jesper L. R. Andersson,Babak A. Ardekani,Babak A. Ardekani,John Ashburner,Brian B. Avants,Ming Chang Chiang,Gary E. Christensen,D. Louis Collins,James C. Gee,Pierre Hellier,Pierre Hellier,Joo Hyun Song,Mark Jenkinson,Claude Lepage,Daniel Rueckert,Paul M. Thompson,Tom Vercauteren,Tom Vercauteren,Roger P. Woods,J. John Mann,Ramin V. Parsey +21 more
TL;DR: This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted and suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols.