Towards Annotation-Free Segmentation of Fluorescently Labeled Cell Membranes in Confocal Microscopy Images

The lack of labeled training data is one of the major challenges in the era of big data and deep learning. Especially for large and complex images, the acquisition of expert annotations becomes infeasible and although many microscopy images contain repetitive and regular structures, manual annotation effort remains expensive. To this end, we propose an approach to obtain image slices and corresponding annotations for confocal microscopy images showing fluorescently labeled cell membranes in an automated and unsupervised manner. Due to their regular structure, cell membrane positions are modeled in silico and respective raw images are synthesized by generative deep learning approaches. The resulting synthesized data set is validated based on the authenticity of generated images and the utilizability for training an existing deep learning segmentation approach. We show, that segmentation accuracy nearly reaches state-of-the-art performance for fluorescently labeled cell membranes in A.thaliana, without the expense of manual labeling.