Year: 2024
Type: conference-paper
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
Institutions: Université de Haute-Alsace, Clemessy (France)
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4403322638