Eli Gibson
Princeton University
107 Papers
314 Citations
Eli Gibson is an academic researcher from Princeton University. The author has contributed to research in topics: Image registration & Computer science. The author has an hindex of 24, co-authored 105 publications. Previous affiliations of Eli Gibson include University of Western Ontario & Lawson Health Research Institute.
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Papers
Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks
Eli Gibson,Francesco Giganti,Yipeng Hu,Ester Bonmati,Steve Bandula,Kurinchi Selvan Gurusamy,Brian R. Davidson,Stephen P. Pereira,Matthew J. Clarkson,Dean C. Barratt +9 more
TL;DR: It is concluded that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.
NiftyNet: a deep-learning platform for medical imaging
Eli Gibson,Wenqi Li,Carole H. Sudre,Lucas Fidon,Dzhoshkun I. Shakir,Guotai Wang,Zach Eaton-Rosen,Robert Gray,Tom Doel,Yipeng Hu,Tom Whyntie,Parashkev Nachev,Marc Modat,Dean C. Barratt,Sebastien Ourselin,M. Jorge Cardoso,Tom Vercauteren +16 more
TL;DR: An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain that facilitates warm starts with established pre-trained networks, adapting existing neural network architectures to new problems, and rapid prototyping of new solutions.
Weakly-supervised convolutional neural networks for multimodal image registration.
Yipeng Hu,Yipeng Hu,Marc Modat,Eli Gibson,Wenqi Li,Nooshin Ghavami,Ester Bonmati,Guotai Wang,Steven Bandula,Caroline M. Moore,Mark Emberton,Sebastien Ourselin,J. Alison Noble,Dean C. Barratt,Tom Vercauteren +14 more
TL;DR: The proposed end‐to‐end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference.
The LUX-ZEPLIN (LZ) experiment
D. S. Akerib,Carl W. Akerlof,D. Yu. Akimov,A. Alquahtani,S. Alsum,T. J. Anderson,N. Angelides,Henrique Araujo,A. Arbuckle,J. E. Armstrong,M. Arthurs,H. Auyeung,X. Bai,A. J. Bailey,J. Balajthy,S. Balashov,J. Bang,M.J. Barry,J. Barthel,D. Bauer,Peter Bauer,A. Baxter,J. Belle,P. Beltrame,J. R. Bensinger,T. Benson,Ethan Bernard,Ethan Bernard,Adam Bernstein,A. Bhatti,A. Biekert,A. Biekert,T. P. Biesiadzinski,B. Birrittella,K. E. Boast,Alexander Bolozdynya,E. M. Boulton,E. M. Boulton,B. Boxer,R. Bramante,S. Branson,P. Brás,Martin Breidenbach,J. H. Buckley,V. Bugaev,R. Bunker,Sergey Burdin,J. Busenitz,J. S. Campbell,C. Carels,Duncan Carlsmith,Ben Carlson,M. C. Carmona-Benitez,Michele Cascella,C. Chan,J. J. Cherwinka,A. A. Chiller,C. Chiller,N. I. Chott,A. Cole,J. P. Coleman,David Colling,R. Conley,A. Cottle,R. Coughlen,W. Craddock,D. Curran,A. Currie,J. E. Cutter,J.P. da Cunha,C. E. Dahl,C. E. Dahl,S. Dardin,Sridhara Dasu,J. C. Davis,T. J. R. Davison,L. de Viveiros,N. Decheine,A. Dobi,J. E. Y. Dobson,E. Druszkiewicz,A. Dushkin,T. K. Edberg,W. R. Edwards,B. N. Edwards,J. Edwards,M. Elnimr,W. T. Emmet,S. R. Eriksen,C. H. Faham,A. Fan,Simon Fayer,S. Fiorucci,Henning Flaecher,I. M. Fogarty Florang,P. Ford,V. B. Francis,F. Froborg,T. Fruth,R. J. Gaitskell,N.J. Gantos,D. Garcia,A. Geffre,V. M. Gehman,R. Gelfand,J. Genovesi,R.M. Gerhard,C. Ghag,Eli Gibson,M. G. D. Gilchriese,S. Gokhale,Bhawna Gomber,T. G. Gonda,A. Greenall,S. Greenwood,G. Gregerson,M. G. D. van der Grinten,Carl Gwilliam,C. R. Hall,D.S. Hamilton,S. Hans,K. Hanzel,T. Harrington,A. Harrison,C. Hasselkus,S. J. Haselschwardt,D. Hemer,S. A. Hertel,John Heise,Seth Hillbrand,O. Hitchcock,C. Hjemfelt,M. Hoff,B. Holbrook,E. Holtom,J. Y.K. Hor,M. Horn,D. Q. Huang,T.W. Hurteau,C. M. Ignarra,M. N. Irving,R. G. Jacobsen,R. G. Jacobsen,O. Jahangir,S. N. Jeffery,W. Ji,Mikkel B. Johnson,J. Johnson,P. Johnson,W. G. Jones,A. C. Kaboth,A. C. Kaboth,A. Kamaha,K. Kamdin,K. Kamdin,V. Kasey,Kareem Kazkaz,J. Keefner,D. Khaitan,M. Khaleeq,A. Khazov,A.V. Khromov,I. Khurana,Yeongduk Kim,W. T. Kim,C. D. Kocher,A. Konovalov,L. Korley,Elena Korolkova,M. Koyuncu,J. Kras,H. Kraus,S. Kravitz,H.J. Krebs,L. Kreczko,Benjamin Krikler,V. A. Kudryavtsev,A. V. Kumpan,S. Kyre,A. Lambert,B. Landerud,N. A. Larsen,A. Laundrie,E. Leason,H. S. Lee,Juhyeong Lee,C. Lee,B. G. Lenardo,David Leonard,R. Leonard,K. T. Lesko,C. Levy,J. Li,Yunpeng Liu,J. Liao,F.-T. Liao,J. Lin,J. Lin,A. Lindote,R. Linehan,W. H. Lippincott,R. Liu,X. Liu,C. Loniewski,M.I. Lopes,B. Lopez Paredes,Wolfgang Lorenzon,D. Lucero,S. Luitz,J. M. Lyle,Candace Lynch,P. Majewski,J. Makkinje,D.C. Malling,A. Manalaysay,Laura Manenti,R. L. Mannino,N. Marangou,D. Markley,P. MarrLaundrie,T.J. Martin,M. F. Marzioni,C. Maupin,C. T. McConnell,Daniel McKinsey,Daniel McKinsey,J. McLaughlin,Dongming Mei,Yue Meng,E. H. Miller,Z. J. Minaker,E. Mizrachi,J. Mock,J. Mock,D. Molash,A. Monte,M. E. Monzani,J. A. Morad,E. Morrison,B. J. Mount,A. St. J. Murphy,D. Naim,A. Naylor,C. Nedlik,C. Nehrkorn,H. N. Nelson,J. Nesbit,F. Neves,J. A. Nikkel,J. A. Nikoleyczik,A. Nilima,J. O'Dell,H. Oh,F. G. O'Neill,K. O’Sullivan,K. O’Sullivan,I. Olcina,M. A. Olevitch,K. C. Oliver-Mallory,K. C. Oliver-Mallory,L. Oxborough,A. Pagac,D. Pagenkopf,S. Pal,K. J. Palladino,V. M. Palmaccio,J. Palmer,M. Pangilinan,S. J. Patton,E. K. Pease,Bjoern Penning,G. Pereira,C. Pereira,I. B. Peterson,A. Piepke,S. Pierson,S. Powell,R. M. Preece,K. Pushkin,Y. Qie,M. Racine,B. N. Ratcliff,J. Reichenbacher,L. Reichhart,C. Rhyne,A. Richards,Q. Riffard,Q. Riffard,G. R. C. Rischbieter,J.P. Rodrigues,H. J. Rose,Richard Rosero,P. Rossiter,R. Rucinski,G. Rutherford,D. Rynders,J.S. Saba,L. Sabarots,D. Santone,M. Sarychev,A. B.M.R. Sazzad,R. W. Schnee,Michael Schubnell,P. R. Scovell,Mary Severson,D. Seymour,S. Shaw,G. W. Shutt,T. A. Shutt,J. J. Silk,C. Silva,K. Skarpaas,W. Skulski,A. R. Smith,Richard J. Smith,Richard J. Smith,R. E. Smith,J. So,M. Solmaz,V. N. Solovov,P. Sorensen,V.V. Sosnovtsev,I. Stancu,M.R. Stark,S. Stephenson,N. Stern,A. Stevens,T.M. Stiegler,K. Stifter,R. Studley,T. J. Sumner,K. Sundarnath,P. Sutcliffe,N. Swanson,Matthew Szydagis,M. Tan,W. C. Taylor,Robert A. Taylor,D. J. Taylor,D. Temples,B. P. Tennyson,P. A. Terman,K.J. Thomas,J. Thomson,D. R. Tiedt,M. Timalsina,W. H. To,A. Tomás,T. Tope,Mani Tripathi,D. R. Tronstad,C. E. Tull,W. Turner,L. Tvrznikova,L. Tvrznikova,M. Utes,U. Utku,S. Uvarov,J. Va’vra,Antonin Vacheret,A. Vaitkus,J.R. Verbus,T. Vietanen,E. Voirin,C. O. Vuosalo,S. Walcott,W.L. Waldron,Kathrin C. Walker,J. J. Wang,Ren-Jie Wang,L. Wang,Yufeng Wang,J. R. Watson,J. R. Watson,J. Migneault,S. Weatherly,R. C. Webb,Wenzhao Wei,M. R. While,Ross G. White,J. T. White,D. White,T. J. Whitis,T. J. Whitis,W. J. Wisniewski,K. Wilson,M. S. Witherell,M. S. Witherell,F.L.H. Wolfs,J. D. Wolfs,D. Woodward,S. D. Worm,X. Xiang,Q. Xiao,Jilei Xu,Minfang Yeh,J. Yin,Ian S. Young,Chao Zhang +398 more
TL;DR: The design and assembly of the LUX-ZEPLIN experiment, a direct detection search for cosmic WIMP dark matter particles, is described and its key design features and requirements are described.
Label-driven weakly-supervised learning for multimodal deformarle image registration
Yipeng Hu,Marc Modat,Eli Gibson,Nooshin Ghavami,Ester Bonmati,Caroline M. Moore,Mark Emberton,J. Alison Noble,Dean C. Barratt,Tom Vercauteren +9 more
- 04 Apr 2018
TL;DR: A weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence is proposed, thereby bypassing classical intensity-based image similarity measures.