  We present a novel method for cell segmentation in microscopy images which is
inspired by the Generative Adversarial Neural Network (GAN) approach. Our
framework is built on a pair of two competitive artificial neural networks,
with a unique architecture, termed Rib Cage, which are trained simultaneously
and together define a min-max game resulting in an accurate segmentation of a
given image. Our approach has two main strengths, similar to the GAN, the
method does not require a formulation of a loss function for the optimization
process. This allows training on a limited amount of annotated data in a weakly
supervised manner. Promising segmentation results on real fluorescent
microscopy data are presented. The code is freely available at:
https://github.com/arbellea/DeepCellSeg.git
