diff --git a/README.md b/README.md index 23f3eb7e0..4ab9b0a84 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,13 @@ DeepTrack is a comprehensive deep learning framework for digital microscopy. We provide tools to create physical simulations of customizable optical systems, to generate and train neural network models, and to analyze experimental data. +If you use DeepTrack 2.0 in your project, please cite our DeepTrack 2.0 article: +``` +Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe. +"Quantitative Digital Microscopy with Deep Learning." +Applied Physics Reviews 8 (2021), 011310. +https://doi.org/10.1063/5.0034891 +``` # Getting started ## Installation @@ -120,14 +127,23 @@ The detailed documentation of DeepTrack 2.0 is available at the following link: ## Cite us! If you use DeepTrack 2.0 in your project, please cite us here: - - Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe. "Quantitative Digital Microscopy with Deep Learning." [https://doi.org/10.1063/5.0034891](https://aip.scitation.org/doi/10.1063/5.0034891) - +``` +Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe. +"Quantitative Digital Microscopy with Deep Learning." +Applied Physics Reviews 8 (2021), 011310. +https://doi.org/10.1063/5.0034891 +``` See also: - - Saga Helgadottir, Aykut Argun, and Giovanni Volpe. "Digital video microscopy enhanced by deep learning." Optica 6.4 (2019): 506-513. [10.1364/OPTICA.6.000506](https://doi.org/10.1364/OPTICA.6.000506) - - Saga Helgadottir, Aykut Argun, and Giovanni Volpe. "DeepTrack." https://github.com/softmatterlab/DeepTrack.git (2019). - +``` +Saga Helgadottir, Aykut Argun, and Giovanni Volpe. +"Digital video microscopy enhanced by deep learning." +Optica 6.4 (2019): 506-513. +https://doi.org/10.1364/OPTICA.6.000506 +``` +``` +Saga Helgadottir, Aykut Argun, and Giovanni Volpe. +"DeepTrack." (2019) +https://github.com/softmatterlab/DeepTrack.git +``` ## Funding This work was supported by the ERC Starting Grant ComplexSwimmers (Grant No. 677511).