@InProceedings{SpinaMartFalc:2016:InMeIm,
               author = "Spina, Thiago Vallin and Martins, Samuel Botter and Falc{\~a}o, 
                         Alexandre Xavier",
          affiliation = "{Institute of Computing - University of Campinas} and {Institute 
                         of Computing - University of Campinas} and {Institute of Computing 
                         - University of Campinas}",
                title = "Interactive Medical Image Segmentation by Statistical Seed 
                         Models",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "Interactive Image Segmentation, Statistical Object Shape Models, 
                         Robot Users.",
             abstract = "Interactive 3D object segmentation is an important and challenging 
                         activity in medical imaging, although it is tedious and 
                         error-prone to be done. Automatic segmentation methods aim to 
                         replace the user altogether, but require user interaction to 
                         produce training data sets of segmented masks and to make error 
                         corrections. We propose a complete framework for interactive 
                         medical image segmentation, which reduces user effort by 
                         automatically providing an initial segmentation result. We develop 
                         a Statistical Seed Model (SSM) to this end, that improves from 
                         seed sets selected by robot users when reconstructing masks of 
                         previously segmented images. The SSM outputs a seed set that may 
                         be used to automatically delineate a new test image. The seeds 
                         provide both an implicit object shape constraint and a flexible 
                         way of interactively correcting segmentation. We demonstrate that 
                         our framework decreases the amount of user interaction by a factor 
                         of three, when segmenting MR-images of the cerebellum.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.045",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.045",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5KRMS",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5KRMS",
           targetfile = "PID4373563.pdf",
        urlaccessdate = "2024, May 01"
}