We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photo-realistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology.
@article{arxiv.1705.00092,
title = {Generative Modeling with Conditional Autoencoders: Building an Integrated Cell},
author = {Gregory R. Johnson and Rory M. Donovan-Maiye and Mary M. Maleckar},
journal= {arXiv preprint arXiv:1705.00092},
year = {2017}
}