With the growth of technology capturing huge amounts of image data has become possible including electron microscopic images. Deep learning techniques have been thus drastically improved for analysing images due to their huge availability. Deep learning has been applied in various domains including medical, biomaterial, engineering fields t o analyze complex images. However, supervised deep learning techniques require huge amounts of annotated images. Annotating the images, specifically pixel wise annotations for segmentation tasks could be overwhelming and requires expert resources. Weakly-supervised learning has been popularly employed in such scenarios where weak labels are used for segmentation purposes. Also, self-supervised learning techniques have greatly reduced the amount of labeled data required to train a model for any downstream task. In this study, we would employ self-supervised learning technique followed by scribble supervision for performing biofilm segmentation on optical images. Our initial classification results and the proposed method for segmentation using scribble annotation are provided in this paper.