Microscopy Cell Segmentation via Adversarial Neural Networks
This work addresses the problem of accurate cell segmentation for microscopy image analysis, offering a weakly supervised approach that reduces the need for extensive annotations, though it appears incremental as it adapts GAN concepts to this specific domain.
The paper tackles cell segmentation in microscopy images by introducing a novel adversarial neural network method called Rib Cage, which trains two competitive networks without a predefined loss function, enabling weakly supervised learning on limited annotated data and showing promising results on real fluorescent microscopy data.
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