%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2024/06.14.00.26
%2 sid.inpe.br/sibgrapi/2024/06.14.00.27
%T Single-Shot Object Detection and Supervised Image Segmentation for Analysing Cell Images Obtainedby Immunohistochemistry
%D 2023
%A Costa, Gustavo Martins Lopes da,
%A Rodrigues, Anna P. C.,
%A Fonseca, Gabriel Barbosa da,
%A Jr, Zenilton K. G. do Patrocínio,
%A Souto, Giovanna Ribeiro,
%A Guimarăes, Silvio Jamil F.,
%@affiliation PUC Minas
%@affiliation PUC Minas
%@affiliation PUC Minas
%@affiliation PUC Minas
%@affiliation PUC Minas
%@affiliation PUC Minas
%E Clua, Esteban Walter Gonzalez,
%E Körting, Thales Sehn,
%E Paulovich, Fernando Vieira,
%E Feris, Rogerio,
%B Conference on Graphics, Patterns and Images, 36 (SIBGRAPI)
%C Rio Grande, RS
%8 Nov. 06-09, 2023
%S Proceedings
%K Image Segmentation, Cell Detection, Computer Vision, Machine Learning,Immunohistochemistry.
%X 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.
%@language en
%3 Single-Shot Object Detection and Supervised ImageSegmentation for Analysing Cell Images Obtainedby Immunohistochemistry.pdf