@InProceedings{RamosCaFaLiNoTrTr:2019:FaSmSe,
               author = "Ramos, Jonathan S. and Cazzolato, Mirela T. and Fai{\c{c}}al, 
                         Bruno S. and Linares, Oscar A. C. and Nogueira-Barbosa, Marcello 
                         H. and Traina Jr., Caetano and Traina, Agma J. M.",
          affiliation = "Institute of Mathematics and Computer Science (ICMC), University 
                         of S{\~a}o Paulo (USP) and Institute of Mathematics and Computer 
                         Science (ICMC), University of S{\~a}o Paulo (USP) and Institute 
                         of Mathematics and Computer Science (ICMC), University of S{\~a}o 
                         Paulo (USP) and Institute of Mathematics and Computer Science 
                         (ICMC), University of S{\~a}o Paulo (USP) and Ribeir{\~a}o Preto 
                         Medical School (FMRP), University of S{\~a}o Paulo (USP) and 
                         Institute of Mathematics and Computer Science (ICMC), University 
                         of S{\~a}o Paulo (USP) and Institute of Mathematics and Computer 
                         Science (ICMC), University of S{\~a}o Paulo (USP)",
                title = "Fast and smart segmentation of paraspinal muscles in magnetic 
                         resonance imaging with CleverSeg",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Segmentation, Muscle, MRI, CleverSeg.",
             abstract = "Magnetic Resonance Imaging (MRI) is a non-invasive technique, 
                         which has been employed to detect and diagnose many spine 
                         pathologies. In a Computer-Aided Diagnosis(CAD) context, the 
                         segmentation of the paraspinal musculature from MRI may support 
                         measurement, quantification, and analysis of muscle-related 
                         pathologies. Current semi-automatic seg-mentation techniques 
                         require too much time from the physicians to annotate all slices 
                         in the exams. In this work, we focus on minimizing the time spent 
                         on manual annotation as well as on the overall segmentation 
                         processing time. We use the mean absolute error between slices 
                         aiming at minimizing the number of annotated slices in each exam. 
                         Moreover, we optimize the manual annotation time by estimating the 
                         inside annotation based on the outside annotation, while the 
                         competitors demand the annotation of inside and outside annotation 
                         (seeds). The experimental evaluation shows that our proposed 
                         approach is able to speed up the manual annotation process in up 
                         to 50%by annotating only a few representative slices, without loss 
                         of accuracy. By annotating only the outside region, the process 
                         can be further speed up by another 50%, reducing the total time to 
                         only 25% of the previously required. Thus, the total time spent on 
                         manual annotation is reduced by up to 75%, and, since human 
                         interaction is greatly diminished, allows a more productive and 
                         less tiresome activity. Despite that, our proposedCleverSeg method 
                         presented accuracy similar to or better than the competitors, 
                         while managing a faster processing time.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00019",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00019",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U39GJB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U39GJB",
           targetfile = "PaperID79.pdf",
        urlaccessdate = "2024, Apr. 28"
}