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		<identifier>8JMKD3MGPAW/3RQEQUE</identifier>
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		<citationkey>AndradeFariCapp:2018:RiDeLe</citationkey>
		<author>Andrade, Natan,</author>
		<author>Faria, Fabio Augusto,</author>
		<author>Cappabianco, Fábio Augusto Menocci,</author>
		<affiliation>Universidade Federal de São Paulo</affiliation>
		<affiliation>Universidade Federal de São Paulo</affiliation>
		<affiliation>Universidade Federal de São Paulo</affiliation>
		<title>A Practical Review on Medical Image Registration: from Rigid to Deep Learning based Approaches</title>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<year>2018</year>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<publisheraddress>Porto Alegre</publisheraddress>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<keywords>Image Registration, Medical Imaging, Deep Learning.</keywords>
		<abstract>The 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.</abstract>
		<language>en</language>
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		<format>On-line</format>
		<size>489 KiB</size>
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		<targetfile>Paper ID Tutorial-1.pdf</targetfile>
		<lastupdate>2018:09.11.00.20.20 sid.inpe.br/banon/2001/03.30.15.38 natandrade18@hotmail.com</lastupdate>
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		<e-mailaddress>natandrade18@hotmail.com</e-mailaddress>
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