@InProceedings{SilvaPinhPithOliv:2020:StToSe,
               author = "Silva, Bernardo Peters Menezes and Pinheiro, La{\'{\i}}s Bastos 
                         and Pithon, Matheus Melo and Oliveira, Luciano Rebou{\c{c}}as 
                         de",
          affiliation = "{Universidade Federal da Bahia} and {Universidade Federal da 
                         Bahia} and {Universidade Estadual do Sudoeste da Bahia} and 
                         {Universidade Federal da Bahia}",
                title = "A study on tooth segmentation and numbering using end-to-end deep 
                         neural networks",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "deep neural networks, instance segmentation and numbering, 
                         panoramic dental X-rays.",
             abstract = "Shape, number, and position of teeth are the main targets of a 
                         dentist when screening for patient's problems on X-rays. Rather 
                         than solely relying on the trained eyes of the dentists, 
                         computational tools have been proposed to aid specialists as 
                         decision supporter for better diagnoses. When applied to X-rays, 
                         these tools are specially grounded on object segmentation and 
                         detection. In fact, the very first goal of segmenting and 
                         detecting the teeth in the images is to facilitate other automatic 
                         methods in further processing steps. Although researches over 
                         tooth segmentation and detection are not recent, the application 
                         of deep learning techniques in the field is new and has not 
                         reached maturity yet. To fill some gaps in the area of dental 
                         image analysis, we bring a thorough study on tooth segmentation 
                         and numbering on panoramic X-ray images by means of end-to-end 
                         deep neural networks. For that, we analyze the performance of four 
                         network architectures, namely, Mask R-CNN, PANet, HTC, and 
                         ResNeSt, over a challenging data set. The choice of these networks 
                         was made upon their high performance over other data sets for 
                         instance segmentation and detection. To the best of our knowledge, 
                         this is the first study on instance segmentation, detection, and 
                         numbering of teeth on panoramic dental X-rays. We found that (i) 
                         it is completely feasible to detect, to segment, and to number 
                         teeth by through any of the analyzed architectures, (ii) 
                         performance can be significantly boosted with the proper choice of 
                         neural network architecture, and (iii) the PANet had the best 
                         results on our evaluations with an mAP of 71.3% on segmentation 
                         and 74.0% on numbering, raising 4.9 and 3.5 percentage points the 
                         results obtained with Mask R-CNN.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00030",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00030",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43B355H",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43B355H",
           targetfile = "paper-camera-ready-final-com-acento.pdf",
        urlaccessdate = "2024, Apr. 27"
}