# Quantitative Digital Microscopy with Deep Learning

arXiv: https://arxiv.org/abs/2010.08260 · HTML: https://mlsanity.com/paper/2010.08260/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Precise localization of corneal reflections in eye images using deep learning trained on synthetic data](https://mlsanity.com/paper/2304.05673/) (2023)
- [LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images](https://mlsanity.com/paper/2309.06129/) (2023)
- [Microplankton life histories revealed by holographic microscopy and deep learning](https://mlsanity.com/paper/2202.09046/) (2022)
- [Deep-learning-powered data analysis in plankton ecology](https://mlsanity.com/paper/2309.08500/) (2023)
- [AnomalousNet: A Hybrid Approach with Attention U-Nets and Change Point Detection for Accurate Characterization of Anomalous Diffusion in Video Data](https://mlsanity.com/paper/2504.05271/) (2025)
- [Single-shot self-supervised particle tracking](https://mlsanity.com/paper/2202.13546/) (2022)
- [Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps](https://mlsanity.com/paper/2210.07102/) (2022)
- [Machine learning for classifying and interpreting coherent X-ray speckle patterns](https://mlsanity.com/paper/2211.08194/) (2022)
- [Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation](https://mlsanity.com/paper/2309.00058/) (2023)

## Abstract (arXiv)

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.