@InProceedings{Gutierrez-CastillaToFaKoScMaMo:2019:ExMuIn,
               author = "Gutierrez-Castilla, Nicol{\'a}s and Torres, Ricardo da Silva and 
                         Falc{\~a}o, Alexandre Xavier and Kozerke, Sebastian and 
                         Schwitter, J{\"u}rg and Masci, Pier-Giorgio and Montoya-Zegarra, 
                         Javier A.",
          affiliation = "Department of Computer Science, San Pablo Catholic University, 
                         Arequipa, Per{\'u} and Institute of Computing, University of 
                         Campinas, Campinas, SP, Brazil and Institute of Computing, 
                         University of Campinas, Campinas, SP, Brazil and Institute for 
                         Biomedical Engineering, ETH Zurich, Zurich, Switzerland and Center 
                         for Cardiac Magnetic Resonance, Lausanne University Hospital, 
                         Lausanne, Switzerland and Rayne Institute School of Bioengineering 
                         and Imaging Sciences, King’s College London, London, United 
                         Kingdom and Institute for Biomedical Engineering, ETH Zurich, 
                         Zurich, Switzerland",
                title = "Long-Range Decoder Skip Connections: Exploiting Multi-Context 
                         Information for Cardiac Image Segmentation",
            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 = "semantic image segmentation, deep learning, cardiac image 
                         analysis, biomedical imaging.",
             abstract = "The heart is one of the most important organs in our body and many 
                         critical diseases are associated with its malfunctioning. To 
                         assess the risk for heart diseases, Magnetic Resonance Imaging 
                         (MRI) has become the golden standard imaging technique, as it 
                         provides to the clinicians stacks of images for analyzing the 
                         heart structures, such as the ventricles, and thus to make a 
                         diagnosis of the patients health. The problem is that examination 
                         of these stacks, often based on the delineation of heart 
                         structures, is tedious and error prone due to inter- and 
                         intra-variability among manual delineations. For this reason,the 
                         investigation of fully automated methods to support heart 
                         segmentation is paramount. Most of the successful methods proposed 
                         to solve this problem are based on deep-learning 
                         solutions.Especially, encoder-decoder architectures, such as the 
                         U-Net [1],have demonstrated to be very effective architectures for 
                         medical image segmentation. In this paper, we propose to use 
                         long-range skip connections on the decoder-part to incorporate 
                         multi-context information onto the predicted segmentation masks 
                         and also to improve the generalization of the models. In addition, 
                         our method obtains smoother segmentations through the combination 
                         of feature maps from different stages onto the final prediction 
                         layer. We evaluate our approach in the ACDC [2] and LVSC [3] heart 
                         segmentation challenges. Experiments performed on both datasets 
                         demonstrate that our approach leads to an improvement on both the 
                         total Dice score and the Ejection Fraction Correlation, when 
                         combined with state-of-the-art encoder-decoder architectures.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00017",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00017",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U39S3S",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U39S3S",
           targetfile = "101.pdf",
        urlaccessdate = "2024, Apr. 27"
}