DeepTrack is an all-in-one deep learning framework for digital microscopy, attempting to bridge the gap between state of the art deep learning solutions and end-users. It provides tools for designing samples, simulating optical systems, training deep learning networks, and analyzing experimental data. We show the versatility of deep learning by solving a wide field of common problems in microscopy. Our hope is to serve as a platform for researchers to launch their solutions for the benifit of the entire field.
Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores – for example, the Dutch Lipid Score – are often employed as alternative, more accessible, albeit less accurate FH diagnostic tools. To overcome the limitations of these traditional methods and to obtain a more reliable approach to FH diagnosis we implement a “virtual” genetic test using machine-learning approaches.
Quantitative analysis of cell structures is essential for pharmaceutical drug screening and medical diagnostics. This work introduces a deep-learning-powered approach to extract quantitative biological information from brightfield microscopy images. Specifically, we train a conditional generative adversarial neural network (cGAN) to virtually stain lipid droplets, cytoplasm, and nuclei from brightfield images of human stem-cell-derived fat cells (adipocytes). Subsequently, we demonstrate that these virtually-stained images can be successfully employed to extract quantitative biologically relevant measures in a downstream cell-profiling analysis. To make this method readily available for future applications, we provide a Python software package that is available online for free access.
In vitro cell culture relies on that the cultured cells thrive and behave in a physiologically relevant way. A standard method to evaluate their behavior is to perform chemical staining in which fluorescent probes are added to the cell culture for further imaging and analysis. However, such technique is invasive and sometimes even toxic to cells, hence, alternative methods are requested. Here, we describe an analysis method for detecting and discriminating live, dead, and apoptotic cells using deep learning. Such an approach will be less labor-intensive than traditional chemical staining procedures and will enable cell imaging with minimal impact.
We present an application of machine learning to deal with the optimization of testing strategies in the event of large-scale epidemic outbreaks. We describe the disease using the archetypal SIR model. Cost-effective containment relies on making the best possible use of the available resources to identify infectious cases. We present a neural-network-powered strategy that adapts to an epidemic without knowing the underlying parameters of the model. The neural network results are more effective than standard approaches, also in the presence of asymptomatic cases. We envision that similar methods can be employed in public health to control epidemic outbreaks.
DeepTrack is an all-in-one deep learning framework for digital microscopy, attempting to bridge the gap between state of the art deep learning solutions and end-users. It provides tools for designing samples, simulating optical systems, training deep learning networks, and analyzing experimental data. Moreover, the framework is packaged with an easy-to-use graphical user interface, designed to solve standard microscopy problems with no required programming experience. By specifically designing the framework with modularity and extendability in mind, we allow new methods to easily be implemented and combined with previous applications.
From the start of digital video microscopy over 20 years ago, single particle tracking has been dominated by algorithmic approaches. These methods are successful at tracking well-defined particles in good imaging conditions but their performance degrades severely in more challenging conditions. To overcome the limitations of traditional algorithmic approaches, data-driven methods using deep learning have been introduced. They managed to successfully track colloidal particles as well as non-spherical biological objects, even in unsteady imaging conditions.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.