DefocusTracker: A Modular Toolbox for Defocusing-based, Single-Camera, 3D Particle Tracking

The need for single-camera 3D particle tracking methods is growing, among others, due to the increasing focus in biomedical research often relying on single-plane microscopy imaging. Defocusing-based methods are ideal for a wide-spread use as they rely on basic microscopy imaging rather than requiring additional non-standard optics. However, a wide-spread use has been limited by the lack of accessible and easy-to-use software. DefocusTracker is an open-source toolbox based on the universal principles of General Defocusing Particle Tracking (GDPT) relying solely on a reference look-up table and image recognition to connect a particle’s image and its respective out-of-plane depth coordinate. The toolbox is built in a modular fashion, allowing for easy addition of new image recognition methods, while maintaining the same workflow and external user interface. DefocusTracker is implemented in MATLAB, while a parallel implementation in Python is in the preparation

General defocusing particle tracking: fundamentals and uncertainty assessment

Abstract: General defocusing particle tracking (GDPT) is a single-camera, three-dimensional particle tracking method that determines the particle depth positions from the defocusing patterns of the corresponding particle images. GDPT relies on a reference set of experimental particle images which is used to predict the depth position of measured particle images of similar shape. While several implementations of the method are possible, its accuracy is ultimately limited by some intrinsic properties of the acquired data, such as the signal-to-noise ratio, the particle concentration, as well as the characteristics of the defocusing patterns. GDPT has been applied in different fields by different research groups; however, a deeper description and analysis of the method fundamentals has hitherto not been available. In this work, we first identity the fundamental elements that characterize a GDPT measurement. Afterward, we present a standardized framework based on synthetic images to assess the performance of GDPT implementations in terms of measurement uncertainty and relative number of measured particles. Finally, we provide guidelines to assess the uncertainty of experimental GDPT measurements, where true values are not accessible and additional image aberrations can lead to bias errors. The data were processed using DefocusTracker, an open-source GDPT software. The datasets were created using the synthetic image generator MicroSIG and have been shared in a freely-accessible repository. Graphic abstract: [Figure not available: see fulltext.]

Defocus particle tracking: A comparison of methods based on model functions, cross-correlation, and neural networks

Defocus particle tracking (DPT) has gained increasing importance for its use to determine particle trajectories in all three dimensions with a single-camera system, as typical for a standard microscope, the workhorse of today's ongoing biomedical revolution. DPT methods derive the depth coordinates of particle images from the different defocusing patterns that they show when observed in a volume much larger than the respective depth of field. Therefore it has become common for state-of-the-art methods to apply image recognition techniques. Two of the most commonly and widely used DPT approaches are the application of (astigmatism) particle image model functions (MF methods) and the normalized cross-correlations between measured particle images and reference templates (CC methods). Though still young in the field, the use of neural networks (NN methods) is expected to play a significant role in future and more complex defocus tracking applications. To assess the different strengths of such defocus tracking approaches, we present in this work a general and objective assessment of their performances when applied to synthetic and experimental images of different degrees of astigmatism, noise levels, and particle image overlapping. We show that MF methods work very well in low-concentration cases, while CC methods are more robust and provide better performance in cases of larger particle concentration and thus stronger particle image overlap. The tested NN methods generally showed the lowest performance, however, in comparison to the MF and CC methods, they are yet in an early stage and have still great potential to develop within the field of DPT

General defocusing particle tracking

A General Defocusing Particle Tracking (GDPT) method is proposed for tracking the three-dimensional motion of particles in Lab-on-a-chip systems based on a set of calibration images and the normalized cross-correlation function. In comparison with other single-camera defocusing particle-tracking techniques, GDPT possesses a series of key advantages: it is applicable to particle images of arbitrary shapes, it is intuitive and easy to use, it can be used without advanced knowledge of optics and velocimetry theory, it is robust against outliers and overlapping particle images, and it requires only equipment which is standard in microfluidic laboratories. We demonstrate the method by tracking the three-dimensional motion of 2 μm spherical particles in a microfluidic channel using three different optical arrangements. The position of the particles was measured with an estimated uncertainty of 0.1 μm in the in-plane direction and 2 μm in the depth direction for a measurement volume of 1510 × 1270 × 160 μm3. A ready-to-use GUI implementation of the method can be acquired on http://www.unibw.de/lrt7/gdpt