# Enhancing high-content imaging for studying microtubule networks at large-scale

arXiv: https://arxiv.org/abs/1910.00662 · HTML: https://mlsanity.com/paper/1910.00662/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [A novel attention mechanism for noise-adaptive and robust segmentation of microtubules in microscopy images](https://mlsanity.com/paper/2507.07800/) (2025)
- [Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search](https://mlsanity.com/paper/2405.19112/) (2024)

## Abstract (arXiv)

Given the crucial role of microtubules for cell survival, many researchers have found success using microtubule-targeting agents in the search for effective cancer therapeutics. Understanding microtubule responses to targeted interventions requires that the microtubule network within cells can be consistently observed across a large sample of images. However, fluorescence noise sources captured simultaneously with biological signals while using wide-field microscopes can obfuscate fine microtubule structures. Such requirements are particularly challenging for high-throughput imaging, where researchers must make decisions related to the trade-off between imaging quality and speed. Here, we propose a computational framework to enhance the quality of high-throughput imaging data to achieve fast speed and high quality simultaneously. Using CycleGAN, we learn an image model from low-throughput, high-resolution images to enhance features, such as microtubule networks in high-throughput low-resolution images. We show that CycleGAN is effective in identifying microtubules with 0.93+ AUC-ROC and that these results are robust to different kinds of image noise. We further apply CycleGAN to quantify the changes in microtubule density as a result of the application of drug compounds, and show that the quantified responses correspond well with known drug effects