Accurate segmentation of live cell images has broad applications in clinical and research contexts. Deep learning methods have been able to perform cell segmentations with high accuracy; however developing machine learning models to do this requires access to high fidelity images of live cells. This is often not available due to resource constraints like limited accessibility to high performance microscopes or due to the nature of the studied organisms. Segmentation on low resolution images of live cells is a difficult task. This paper proposes a method to perform live cell segmentation with low resolution images by performing super-resolution as a pre-processing step in the segmentation pipeline.
@article{arxiv.2212.02028,
title = {Double U-Net for Super-Resolution and Segmentation of Live Cell Images},
author = {Mayur Bhandary and J. Patricio Reyes and Eylul Ertay and Aman Panda},
journal= {arXiv preprint arXiv:2212.02028},
year = {2022}
}