Package: deeptrack
A comprehensive deep learning framework for digital microscopy.Installationâ€¢Examplesâ€¢Basicsâ€¢Cite usâ€¢LicenseWe provide tools to create physical simulations of optical systems, to generate and train neural network models, and to analyze experimental data.InstallationDeepTrack 2.1 requires at least python 3.6.To install DeepTrack 2.1, open a terminal or command prompt and run:pip install deeptrackIf you have a very recent version of python, you may need to install numpybeforeDeepTrack. This is a known issue with scikit-image.Updating to 2.1 from 2.0If you are already using DeepTrack 2.0 (pypi version 0.x.x), updating to DeepTrack 2.1 (pypi version 1.x.x) is painless. If you have followed deprecation warnings, no change to your code is needed. There are two breaking changes:The deprecated operator+to chain features has been removed. It is now only possible using the>>operator.The deprecated operator**to duplicate a feature has been removed. It is now only possible using the^operator.If you notice any other changes in behavior, please report it to us in the issues tab.Examples of applications using DeepTrackDeepTrack is a general purpose deep learning framework for microscopy, meaning you can use it for any task you like. Here, we show some common applications!Single particle trackingTraining a CNN-based single particle tracker using simulated dataUnsupervised training of a single particle tracker using LodeSTARMulti-particle trackingTraining LodeSTAR to detect multiple cells from a single imageTraining a UNet-based multi-particle tracker using simulated dataParticle tracingTraining MAGIK to trace migrating cellsBasics to learn DeepTrack 2.1Everybody learns in different ways! Depending on your preferences, and what you want to do with DeepTrack, you may want to check out one or more of these resources.Getting-started guidesWe have two separate series of notebooks which aims to teach you all you need to know to use DeepTrack to its fullest. The first is a set of six notebooks with a focus on the application.deeptrack_introduction_tutorialgives an overview of how to use DeepTrack 2.1.tracking_particle_cnn_tutorialdemonstrates how to track a point particle with a convolutional neural network (CNN).tracking_particle_cnn_tutorialdemonstrates how to track multiple particles using a U-net.characterizing_aberrations_tutorialdemonstrates how to add and characterize aberrations of an optical device.distinguishing_particles_in_brightfield_tutorialdemonstrates how to use a U-net to track and distinguish particles of different sizes in brightfield microscopy.analyzing_video_tutorialdemonstrates how to create videos and how to train a neural network to analyze them.The second series focuses on individual topics, introducing them in a natural order.Introducing how to create simulation pipelines and train models.Demonstrating data generators.Demonstrating how to customize models using layer-blocks.DeepTrack 2.1 in actionAdditionally, we have seven more case studies which are less documented, but gives additional insight in how to use DeepTrack with real datasetsMNISTclassifies handwritted digits.single particle trackingtracks experimentally captured videos of a single particle. (Requires opencv-python compiled with ffmpeg to open and read a video.)single particle sizingextracts the radius and refractive index of particles.multi-particle trackingdetects quantum dots in a low SNR image.3-dimensional trackingtracks particles in three dimensions.cell countingcounts the number of cells in fluorescence images.GAN image generationuses a GAN to create cell image from masks.Model-specific examplesWe also have examples that are specific for certain models. This includesLodeSTARfor label-free particle tracking.MAGIKfor graph-based particle linking and trace characterization.DocumentationThe detailed documentation of DeepTrack 2.1 is available at the following link:https://softmatterlab.github.io/DeepTrack2/deeptrack.htmlVideo TutorialsVideos are currently being updated to match with the current version of DeepTrack.Cite us!If you use DeepTrack 2.1 in your project, please cite us here:Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, JesÃºs Pineda, Daniel Midtvedt, Giovanni Volpe.
"Quantitative Digital Microscopy with Deep Learning."
Applied Physics Reviews 8 (2021), 011310.
https://doi.org/10.1063/5.0034891See also:https://www.nature.com/articles/s41467-022-35004-y:Midtvedt, B., Pineda, J., SkÃ¤rberg, F. et al. 
"Single-shot self-supervised object detection in microscopy." 
Nat Commun 13, 7492 (2022).https://arxiv.org/abs/2202.06355:JesÃºs Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio NoÃ©, Daniel  Midtvedt, Giovanni Volpe,1 and  Carlo  Manzo
"Geometric deep learning reveals the spatiotemporal fingerprint ofmicroscopic motion."
arXiv 2202.06355 (2022).https://doi.org/10.1364/OPTICA.6.000506:Saga Helgadottir, Aykut Argun, and Giovanni Volpe.
"Digital video microscopy enhanced by deep learning."
Optica 6.4 (2019): 506-513.https://github.com/softmatterlab/DeepTrack.git:Saga Helgadottir, Aykut Argun, and Giovanni Volpe.
"DeepTrack." (2019)FundingThis work was supported by the ERC Starting Grant ComplexSwimmers (Grant No. 677511), the ERC Starting Grant MAPEI (101001267), and the Knut and Alice Wallenberg Foundation.