@InProceedings{JaderFonRuiPitOli:2018:DeInSe,
               author = "Jader, Gil and Fontinele, Jefferson and Ruiz, Marco and Pithon, 
                         Matheus and Oliveira, Luciano",
          affiliation = "UFBA and UFBA and UFBA and UESC and UFBA",
                title = "Deep instance segmentation of teeth in panoramic X-ray images",
            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 = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "instance segmentation, tooth segmentation, panoramic X-ray 
                         image.",
             abstract = "In dentistry, radiological examinations help specialists by 
                         showing structure of the tooth bones with the goal of screening 
                         embedded teeth, bone abnormalities, cysts, tumors, infections, 
                         fractures, problems in the temporomandibular regions, just to cite 
                         a few. Sometimes, relying solely in the specialist's opinion can 
                         bring differences in the diagnoses, which can ultimately hinder 
                         the treatment. Although tools for complete automatic diagnosis are 
                         no yet expected, image pattern recognition has evolved towards 
                         decision support, mainly starting with the detection of teeth and 
                         their components in X-ray images. Tooth detection has been object 
                         of research during at least the last two decades, mainly relying 
                         in threshold and region-based methods. Following a different 
                         direction, this paper proposes to explore a deep learning method 
                         for instance segmentation of the teeth. To the best of our 
                         knowledge, it is the first system that detects and segment each 
                         tooth in panoramic X-ray images. It is noteworthy that this image 
                         type is the most challenging one to isolate teeth, since it shows 
                         other parts of patient's body (e.g., chin, spine and jaws). We 
                         propose a segmentation system based on mask region-based 
                         convolutional neural network to accomplish an instance 
                         segmentation. Performance was thoroughly assessed from a 1500 
                         challenging image data set, with high variation and containing 10 
                         categories of different types of buccal image. By training the 
                         proposed system with only 193 images of mouth containing 32 teeth 
                         in average, using transfer learning strategies, we achieved 98% of 
                         accuracy, 88% of F1-score, 94% of precision, 84% of recall and 99% 
                         of specificity over 1224 unseen images, results very superior than 
                         other 10 unsupervised methods.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00058",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00058",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RNF7US",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RNF7US",
           targetfile = "tooth_segmentation.pdf",
        urlaccessdate = "2024, Apr. 30"
}