  Background: Accurate segmentation of microscopic structures such as
bio-artificial capsules in microscopy imaging is a prerequisite to the
computer-aided understanding of important biomechanical phenomenons.
State-of-the-art segmentation performances are achieved by deep neural networks
and related data-driven approaches. Training these networks from only a few
annotated examples is challenging while producing manually annotated images
that provide supervision is tedious.
  Method: Recently, self-supervision, i.e. designing a neural pipeline
providing synthetic or indirect supervision, has proved to significantly
increase generalization performances of models trained on few shots. The
objective of this paper is to introduce one such neural pipeline in the context
of micro-capsule image segmentation. Our method leverages the rather simple
content of these images so that a trainee network can be mentored by a referee
network which has been previously trained on synthetically generated pairs of
corrupted/correct region masks.
  Results: Challenging experimental setups are investigated. They involve from
only 3 to 10 annotated images along with moderately large amounts of
unannotated images. In a bio-artificial capsule dataset, our approach
consistently and drastically improves accuracy. We also show that the learnt
referee network is transferable to another Glioblastoma cell dataset and that
it can be efficiently coupled with data augmentation strategies.
  Conclusions: Experimental results show that very significant accuracy
increments are obtained by the proposed pipeline, leading to the conclusion
that the self-supervision mechanism introduced in this paper has the potential
to replace human annotations.
