@InProceedings{OliveiraPePiFeTaBlCe:2021:AuSePo,
               author = "Oliveira, Hugo Neves de and Penteado, Larissa de Oliveira and 
                         Pimenta, Jos{\'e} Luiz Maciel and Ferraciolli, Suely Fazio and 
                         Takahashi, Marcelo Straus and Bloch, Isabelle and Cesar Junior, 
                         Roberto Marcondes",
          affiliation = "{Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - 
                         Universidade de S{\~a}o Paulo } and {Instituto de Matem{\'a}tica 
                         e Estat{\'{\i}}stica - Universidade de S{\~a}o Paulo } and 
                         {Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - 
                         Universidade de S{\~a}o Paulo } and {Faculdade de Medicina - 
                         Universidade de S{\~a}o Paulo } and {Faculdade de Medicina - 
                         Universidade de S{\~a}o Paulo } and {Sorbonne Universite } and 
                         {Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - 
                         Universidade de S{\~a}o Paulo}",
                title = "Automatic Segmentation of Posterior Fossa Structures in Pediatric 
                         Brain MRIs",
            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 = "biomedical image segmentation, posterior fossa structures, deep 
                         learning.",
             abstract = "Pediatric brain MRI is a useful tool in assessing the healthy 
                         cerebral development of children. Since many pathologies may 
                         manifest in the brainstem and cerebellum, the objective of this 
                         study was to have an automated segmentation of pediatric posterior 
                         fossa structures. These pathologies include a myriad of etiologies 
                         from congenital malformations to tumors, which are very prevalent 
                         in this age group. We propose a pediatric brain MRI segmentation 
                         pipeline composed of preprocessing, semantic segmentation and 
                         post-processing steps. Segmentation modules are composed of two 
                         ensembles of networks: generalists and specialists. The generalist 
                         networks are responsible for locating and roughly segmenting the 
                         brain areas, yielding regions of interest for each target organ. 
                         Specialist networks can then improve the segmentation performance 
                         for underrepresented organs by learning only from the regions of 
                         interest from the generalist networks. At last, post-processing 
                         consists in merging the specialist and generalist networks 
                         predictions, and performing late fusion across the distinct 
                         architectures to generate a final prediction. We conduct a 
                         thorough ablation analysis on this pipeline and assess the 
                         superiority of the methodology in segmenting the brain stem, 4th 
                         ventricle and cerebellum. The proposed methodology achieved a 
                         macro-averaged Dice index of 0.855 with respect to manual 
                         segmentation, with only 32 labeled volumes used during training. 
                         Additionally, average distances between automatically and manually 
                         segmented surfaces remained around 1mm for the three structures, 
                         while volumetry results revealed high agreement between manually 
                         labeled and predicted regions.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00025",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00025",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CUN9S",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CUN9S",
           targetfile = "SIBGRAPI_2021_Segmentation_ICr_Camera_Ready.pdf",
        urlaccessdate = "2024, May 06"
}