@InProceedings{AndradeFariCapp:2018:RiDeLe,
               author = "Andrade, Natan and Faria, Fabio Augusto and Cappabianco, 
                         F{\'a}bio Augusto Menocci",
          affiliation = "{Universidade Federal de S{\~a}o Paulo} and {Universidade Federal 
                         de S{\~a}o Paulo} and {Universidade Federal de S{\~a}o Paulo}",
                title = "A Practical Review on Medical Image Registration: from Rigid to 
                         Deep Learning based Approaches",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Image Registration, Medical Imaging, Deep Learning.",
             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.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00066",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00066",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RQEQUE",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RQEQUE",
           targetfile = "Paper ID Tutorial-1.pdf",
        urlaccessdate = "2024, Apr. 28"
}