Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVES: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-hematological diseases. However, the variability in staining protocols across different laboratories can affect the color of images and performance of automatic recognition models. The objective of this work is to develop, train and evaluate a new system for the normalization of color staining of peripheral blood cell images, so that it transforms images from different centers to map the color staining of a reference center (RC) while preserving the structural morphological features.

Authors

  • Kevin Barrera
    Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain. Electronic address: kevin.barrera@upc.edu.
  • José Rodellar
    Mathematics, EEBE, Technical University of Catalonia, Barcelona, Catalonia, Spain.
  • Santiago Alférez
    Mathematics, EEBE, Technical University of Catalonia, Barcelona, Catalonia, Spain.
  • Anna Merino
    Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic of Barcelona, Barcelona, Catalonia, Spain amerino@clinic.cat.