# Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning

arXiv: https://arxiv.org/abs/2207.00089 · HTML: https://mlsanity.com/paper/2207.00089/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 67 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://mlsanity.com/paper/1705.04286/) (2017)
- [Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning](https://mlsanity.com/paper/2001.10695/) (2020)
- [Single-shot autofocusing of microscopy images using deep learning](https://mlsanity.com/paper/2003.09585/) (2020)
- [Deep learning-based super-resolution in coherent imaging systems](https://mlsanity.com/paper/1810.06611/) (2018)
- [Deep learning approach to coherent noise reduction in optical diffraction tomography](https://mlsanity.com/paper/1811.06950/) (2018)
- [High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition](https://mlsanity.com/paper/2002.07377/) (2020)
- [Deep Learning-enabled Detection and Classification of Bacterial Colonies using a Thin Film Transistor (TFT) Image Sensor](https://mlsanity.com/paper/2205.03549/) (2022)
- [Background Subtraction using Adaptive Singular Value Decomposition](https://mlsanity.com/paper/1906.12064/) (2019)

## Built on by (most cited first, 6 of 6)

- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor](https://mlsanity.com/paper/2403.11035/) (2024)
- [Deep Learning-based Detection of Bacterial Swarm Motion Using a Single Image](https://mlsanity.com/paper/2410.15229/) (2024)
- [Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning](https://mlsanity.com/paper/2601.18219/) (2026)
- [Continuous quantification of viral plaque dynamics using ultra-large-area label-free imaging enables rapid antiviral susceptibility testing](https://mlsanity.com/paper/2605.01738/) (2026)
- [End-to-end plaque counting and virus titration from laboratory plate images with deep learning](https://mlsanity.com/paper/2605.16008/) (2026)

## Abstract (arXiv)

We present a rapid and stain-free quantitative viral plaque assay using lensfree holographic imaging and deep learning. This cost-effective, compact, and automated device significantly reduces the incubation time needed for traditional plaque assays while preserving their advantages over other virus quantification methods. This device captures ~0.32 Giga-pixel/hour phase information of the objects per test well, covering an area of ~30x30 mm^2, in a label-free manner, eliminating staining entirely. We demonstrated the success of this computational method using vesicular stomatitis virus (VSV), herpes simplex virus (HSV-1) and encephalomyocarditis virus (EMCV). Using a neural network, this stain-free device automatically detected the first cell lysing events due to the VSV viral replication as early as 5 hours after the incubation, and achieved >90% detection rate for the VSV plaque-forming units (PFUs) with 100% specificity in <20 hours, providing major time savings compared to the traditional plaque assays that take at least 48 hours. Similarly, this stain-free device reduced the needed incubation time by ~48 hours for HSV-1 and ~20 hours for EMCV, achieving >90% detection rate with 100% specificity. We also demonstrated that this data-driven plaque assay offers the capability of quantifying the infected area of the cell monolayer, performing automated counting and quantification of PFUs and virus-infected areas over a 10-fold larger dynamic range of virus concentration than standard viral plaque assays. This compact, low-cost, automated PFU quantification device can be broadly used in virology research, vaccine development, and clinical applications.