@InProceedings{DuarteGobbFrayCarv:2020:DeAlDi,
               author = "Duarte, Kau{\^e} Tartarotti Nepomuceno and Gobbi, David and 
                         Frayne, Richard and Carvalho, Marco Antonio Garcia de",
          affiliation = "School of Technology, University of Campinas and Calgary Image 
                         Processing and Analysis Centre, Departments of Radiology and 
                         Clinical Neurosciences, Hotchkiss Brain Institute, University of 
                         Calgary and Calgary Image Processing and Analysis Centre, 
                         Departments of Radiology and Clinical Neurosciences, Hotchkiss 
                         Brain Institute, University of Calgary and School of Technology, 
                         University of Campinas",
                title = "Detecting Alzheimer’s Disease based on Structural Region Analysis 
                         using a 3D Shape Descriptor",
            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 = "shape descriptor, alzheimer, image segmentation, similarity, brain 
                         analysis.",
             abstract = "Alzheimers disease (AD) is a common neurodegenerative dementia 
                         that affects older people. Changes in behavior and cognition are 
                         the most common characteristics of this disease and are associated 
                         with changes in brain structure. Techniques focusing on brain 
                         shape have been recently proposed to quantify and understand these 
                         changes. One challenge when examining AD is that each anatomical 
                         region may have a unique role in and time course for brain 
                         deterioration, requiring a whole-brain method that is capable of 
                         individual (or regional) analyses at different disease stages. We 
                         propose to apply the scale-invariant heat kernel signature 
                         descriptor to magnetic resonance brain images in order to evaluate 
                         regional shape features across different brain regions. We 
                         measured the shape feature similarity in 500 subjects, equally 
                         divided across five progressive, disease-based stages. The shape 
                         analysis provided a complementary perspective to whole-brain 
                         analysis, due to the capability of identifying how different 
                         structures degenerate at different rates in the brain. In total, a 
                         group of 99 distinct brain regions belonging to cortical and deep 
                         gray matter were analyzed across the five disease stages. 
                         Preliminary assessment of shape-based analysis of key brain 
                         regions demonstrated that SIHKS was predictive of disease stage 
                         and disease progression.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00032",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00032",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43B6D2H",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43B6D2H",
           targetfile = "Sibgrapi_2020___Kaue_TND_CAMERAREADY.pdf",
        urlaccessdate = "2024, May 02"
}