# Microscopy Cell Segmentation via Adversarial Neural Networks

arXiv: https://arxiv.org/abs/1709.05860 · HTML: https://mlsanity.com/paper/1709.05860/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2017.

## Built on by (most cited first, 4 of 4)

- [Edge-competing Pathological Liver Vessel Segmentation with Limited Labels](https://mlsanity.com/paper/2108.00384/) (2021)
- [Boundary Knowledge Translation based Reference Semantic Segmentation](https://mlsanity.com/paper/2108.01075/) (2021)
- [Visual Boundary Knowledge Translation for Foreground Segmentation](https://mlsanity.com/paper/2108.00379/) (2021)
- [Boundary-Aware Instance Segmentation in Microscopy Imaging](https://mlsanity.com/paper/2603.21206/) (2026)

## Abstract (arXiv)

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