T
Tina Kapur
Researcher at Brigham and Women's Hospital
Publications - 117
Citations - 4595
Tina Kapur is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 30, co-authored 108 publications receiving 3805 citations. Previous affiliations of Tina Kapur include Queen Elizabeth Hospital Birmingham & Nanjing University.
Papers
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Patent
Fluoroscopic tracking and visualization system
TL;DR: In this article, a system employs a tracker and a set of substantially non-shadowing point markers, arranged in a fixed pattern or set in a fluoroscope calibration fixture that is imaged in each shot.
Journal ArticleDOI
Segmentation of Brain Tissue from Magnetic Resonance Images
TL;DR: This work presents a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the computer vision literature: expectation/maximization segmentation, binary mathematical morphology, and active contour models.
Journal ArticleDOI
OpenIGTLink: an open network protocol for image-guided therapy environment
Junichi Tokuda,Gregory S. Fischer,Xenophon Papademetris,Ziv Yaniv,Luis Ibanez,Patrick Cheng,Haiying Liu,Jack Blevins,Jumpei Arata,Alexandra J. Golby,Tina Kapur,Steve Pieper,Everette C. Burdette,Gabor Fichtinger,Clare M. Tempany,Nobuhiko Hata +15 more
TL;DR: With increasing research on system integration for image‐guided therapy (IGT), there has been a strong demand for standardized communication among devices and software to share data such as target positions, images and device status.
Book ChapterDOI
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Mohsen Ghafoorian,Mohsen Ghafoorian,Alireza Mehrtash,Alireza Mehrtash,Tina Kapur,Nico Karssemeijer,Elena Marchiori,Mehran Pesteie,Charles R.G. Guttmann,Frank-Erik de Leeuw,Clare M. Tempany,Bram van Ginneken,Andriy Fedorov,Purang Abolmaesumi,Bram Platel,William M. Wells +15 more
TL;DR: In this paper, a CNN was trained on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain, and compared the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.
Journal ArticleDOI
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Jan Egger,Tina Kapur,Andriy Fedorov,Steve Pieper,James V. Miller,Harini Veeraraghavan,Bernd Freisleben,Alexandra J. Golby,Christopher Nimsky,Ron Kikinis +9 more
TL;DR: In this study, 4 physicians segmented glioblastoma multiforme patients in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis.