Microscopy Cell Segmentation via Adversarial Neural Networks
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We present a novel approach for the segmentation of microscopy images. This method utilizes recent development in the field of Deep Artificial Neural Networks in general and specifically the advances in Generative Adversarial Neural Networks (GAN). We propose a pair of two competitive networks which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. The system is an expansion of the well know GAN model to conditional probabilities given an input image. This approach has two main strengths as it is weakly supervised, i.e. can be easily trained on a limited amount of data, and does not require a definition of a loss function for the optimization. Promising results are presented. The code is freely available at: https://github.com/arbellea/DeepCellSeg.git
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