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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3RQEQUE
Repositorysid.inpe.br/sibgrapi/2018/09.11.00.20
Last Update2018:09.11.00.20.20 natandrade18@hotmail.com
Metadatasid.inpe.br/sibgrapi/2018/09.11.00.20.20
Metadata Last Update2020:02.20.22.06.48 administrator
Citation KeyAndradeFariCapp:2018:RiDeLe
TitleA Practical Review on Medical Image Registration: from Rigid to Deep Learning based Approaches
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 02
Number of Files1
Size489 KiB
Context area
Author1 Andrade, Natan
2 Faria, Fabio Augusto
3 Cappabianco, Fábio Augusto Menocci
Affiliation1 Universidade Federal de São Paulo
2 Universidade Federal de São Paulo
3 Universidade Federal de São Paulo
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressnatandrade18@hotmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-09-11 00:20:20 :: natandrade18@hotmail.com -> administrator ::
2020-02-20 22:06:48 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Transferable1
Tertiary TypeTutorial
KeywordsImage Registration, Medical Imaging, Deep Learning.
AbstractThe large variety of medical image modalities (e.g. Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography) acquired from the same body region of a patient together with recent advances in computer architectures with faster and larger CPUs and GPUs allows a new, exciting, and unexplored world for image registration area. A precise and accurate registration of images makes possible understanding the etiology of diseases, improving surgery planning and execution, detecting otherwise unnoticed health problem signals, and mapping functionalities of the brain. The goal of this paper is to present a review of the state-of-the-art in medical image registration starting from the preprocessing steps, covering the most popular methodologies of the literature and finish with the more recent advances and perspectives from the application of Deep Learning architectures.
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Languageen
Target FilePaper ID Tutorial-1.pdf
User Groupnatandrade18@hotmail.com
Visibilityshown
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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