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.
Digital Holographic Microscopy (DHM) has been a successful imaging technique for various applications in biomedical imaging, particle analysis, and optical engineering. Though DHM has been successful in reconstructing 3D volumes with stationary objects, it has still been a challenging task to track fast mobile objects. Recent advancements in deep learning with convolutional neural networks have been proven useful in solving experimental difficulties, starting from tracking single particles to multiple bacterial cells. Here, we propose a compact DHM driven by neural networks with a minimal amount of optical elements with an ultimate aim for easy usage and transportation.
Particles with dimensions smaller than the wavelength of visible light are essential in many fields. As particle size and composition greatly influence particle function, fast and accurate characterisation of these properties is important. Traditional approaches use the Brownian motion of the particles to deduce their size, and therefore requires to observe the particles for many consecutive time-steps. In addition, such techniques can only be applied in environments with known viscosity, hindering characterization in complex environments.
In this work, we demonstrate characterisation of subwavelength particle size and refractive index surpassing that of traditional methods using two orders of magnitude fewer observations of each particle, with no reference to particle motion. This opens up the possibility to characterise and temporally resolve the properties of subwavelength particles in complex environments where the relation between particle dynamics and size is unknown.