Data augmentation for pathogen segmentation in vinewood fluorescence microscopy images

2024/10/14 by Julie Munsch, Munsch, Julie, Sonia Ouali +7
Biochemistry, Genetics and Molecular Biology · Chemistry · #Cell Image Analysis Techniques #Spectroscopy and Chemometric Analyses #data augmentation #deep learning #fluorescence microscopy #image segmentation #machine learning

paper · pdf · doi:10.60643/urai.v2024p153

openalex publication_date 2024/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

In this paper, we address the problem of segmentation of pathogens within fluorescence microscopy images. To our knowledge, the quantification from such images is an original problem. As a consequence, there is no available database to rely upon in order to use supervised machine learning techniques. In this paper, we provide a workaround by creating realistic images containing the desired filamentary pattern and variable blur effect. Numerical results show the interest of this data augmentation technique, especially on images corresponding to a difficult segmentation. , image segmentation, fluorescence microscopy deep learning, machine

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