Beschreibung
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.
Hauptautor
Frau
Julie Munsch
(Eiffage energie systemes et IRIMAS, Université de Haute Alsace)
Co-Autoren
Dr.
Sonia Ouali
(IRIMAS, Université de Haute Alsace)
Dr.
Jean-Baptiste Courbot
(IRIMAS, Université de Haute Alsace)
Dr.
Romain Pierron
(LVBE Université de Haute-Alsace)
Prof.
Olivier Haeberlé
(IRIMAS, Université de Haute Alsace)
