Microscopy Cell Segmentation via Adversarial Neural Networks

TL;DR

This paper introduces a novel adversarial neural network framework called Rib Cage for microscopy cell segmentation, enabling effective training with limited annotated data and achieving promising results on real microscopy images.

Contribution

The paper proposes a unique GAN-inspired architecture for cell segmentation that does not require explicit loss functions, facilitating weakly supervised training with limited data.

Findings

Effective segmentation on real microscopy data

No need for explicit loss function formulation

Works well with limited annotated data

Abstract

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