@InProceedings{CostaRoFoJrSoGu:2023:SiObDe,
               author = "Costa, Gustavo Martins Lopes da and Rodrigues, Anna P. C. and 
                         Fonseca, Gabriel Barbosa da and Jr, Zenilton K. G. do 
                         Patroc{\'{\i}}nio and Souto, Giovanna Ribeiro and 
                         Guimar{\~a}es, Silvio Jamil F.",
          affiliation = "{PUC Minas} and {PUC Minas} and {PUC Minas} and {PUC Minas} and 
                         {PUC Minas} and {PUC Minas}",
                title = "Single-Shot Object Detection and Supervised Image Segmentation for 
                         Analysing Cell Images Obtainedby Immunohistochemistry",
            booktitle = "Proceedings...",
                 year = "2023",
               editor = "Clua, Esteban Walter Gonzalez and K{\"o}rting, Thales Sehn and 
                         Paulovich, Fernando Vieira and Feris, Rogerio",
         organization = "Conference on Graphics, Patterns and Images, 36. (SIBGRAPI)",
             keywords = "Image Segmentation, Cell Detection, Computer Vision, Machine 
                         Learning,Immunohistochemistry.",
             abstract = "Analyzing cell images and identifying them correctly is a 
                         fundamental task in the immunohistochemical exam. In this paper we 
                         propose a novel method to segment FoxP3+ Regulatory T cells (Treg) 
                         images automatically, in order to assist healthcare professionals 
                         in the task of identifying and counting potentially cancerous 
                         cells. The proposed method relies on combining an object detection 
                         network, which is tailor-made for microscopy images, with a 
                         marker-based image segmentation method to produce the final 
                         segmentation, while requiring only a 50x50 training patch to do 
                         so. Our pipeline consists on predicting the location of the cells, 
                         applying morphological operations on the prediction weights to 
                         transform them into markers, and finally using the segmentation 
                         method iDISF to generate high quality segmentations. We also 
                         propose a new FoxP3+ Treg cells dataset containing 10 high 
                         resolution images, with a qualitative and quantitative analysis of 
                         our segmentation methods for this dataset.",
  conference-location = "Rio Grande, RS",
      conference-year = "Nov. 06-09, 2023",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/4BFC5SP",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/4BFC5SP",
           targetfile = "Single-Shot Object Detection and Supervised ImageSegmentation for 
                         Analysing Cell Images Obtainedby Immunohistochemistry.pdf",
        urlaccessdate = "2024, Sep. 08"
}