Data augmentation for pathogen segmentation in vinewood fluorescence microscopy images

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