@InProceedings{DinizSilvPaiv:2021:MeSeSp,
               author = "Diniz, Jo{\~a}o Ot{\'a}vio Bandeira and Silva, Arist{\'o}fanes 
                         Corr{\^e}a and Paiva, Anselmo Cardoso de",
          affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e 
                         Tecnologia do Maranh{\~a}o and {Universidade Federal do 
                         Maranh{\~a}o} and {Universidade Federal do Maranh{\~a}o}",
                title = "Methods for segmentation of spinal cord and esophagus in 
                         radiotherapy planning computed tomography",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Computed Tomography, Esophagus, Spinal Cord, OAR, Deep Learning.",
             abstract = "Organs at Risk (OARs) are healthy tissues around cancer that must 
                         be preserved in radiotherapy (RT). The spinal cord and esophagus 
                         are crucial OARs. In this work, we proposed methods for the 
                         segmentation of these OARs from the CT using image processing 
                         techniques and deep convolutional neural network (CNN). For spinal 
                         cord segmentation, two methods are proposed, the first using 
                         techniques such as template matching, superpixel, and CNN. The 
                         second method, use adaptive template matching and CNN. In the 
                         esophagus segmentation, we proposed a method composed of 
                         registration techniques, atlas, pre-processing, U-Net, and 
                         post-processing. The methods were applied to 36 planning CT images 
                         provided by The Cancer Imaging Archive. The first method for 
                         spinal cord segmentation obtained 78.20\% Dice. The second method 
                         for spinal cord segmentation obtained 81.69\% Dice. The esophagus 
                         segmentation method obtained an accuracy of 82.15\% Dice.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EDUCE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EDUCE",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 06"
}