@InProceedings{AndradeFariCapp:2021:ImSiMe,
               author = "Andrade, Natan and Faria, Fabio A. and Cappabianco, F{\'a}bio 
                         A.",
          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 = "Improving Similarity Metric of Multi-modal MR Brain Image 
                         Registration Via a Deep Ensemble",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image registration, similarity metric, ensemble, brain imaging, 
                         MRI.",
             abstract = "Brain image registration fuses and aligns sets of structural or 
                         functional images within individual and population studies. The 
                         similarity metric is an image registration component used for 
                         detecting the same target region in different images. Multi-modal 
                         image registration constitutes one of the greatest challenges in 
                         medical imaging as it adds even more variability to the tissue and 
                         organ appearance, shape, and positioning. This paper contains two 
                         contributions to solve this complex problem: (1) we propose a 
                         solution to compute the similarity metric based on a deep ensemble 
                         method. It combines multiple traditional and deep similarity 
                         metrics into a single improved similarity map; (2) we propose 
                         novel evaluation metrics to validate the results. Experiment 
                         results in the context of T1- and T2-weighted MR images of the 
                         human brain show a major improvement to the state-of-the-art, 
                         especially in reducing the false-positive region occurrences.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00023",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00023",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CUNC5",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CUNC5",
           targetfile = "ID 92.pdf",
        urlaccessdate = "2024, May 06"
}