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.

The use of single-camera 3D particle tracking analysis is receiving increasing interest, among others, due to the rapid development of bio-engineering and biomedical sciences where single-access imaging, such as with microscopes, is a standard research tool [

To accommodate this need, we developed

The general architecture and workflow is shown in

As seen in

With a trained

An example of typical

The

Method 0 is used for 2D particle tracking based on setting a boundary intensity threshold to detect particles. This method provides a quick way to perform 2D tracking, which e.g. can be useful in the creation of training dataset using images with in-plane particle motion.

Method 1 performs the full defocusing-based 3D particle tracking, for a full description of Method 1, we refer to [_{m}. The values of _{m} can range from 0 to 1, with 1 corresponding to a perfect match between the target image and a calibration image.

In order to integrate a new method into the

Here, all the parameters in the model structure must be organized in the following three mandatory subfields:

Overview of data structures and functions in the

The MATLAB toolbox contains three work-through examples (WTE0, WTE1, and WTE2) that serve as tutorials to get new users quickly started, but also as test scripts in case new functionalities or methods are added. The scripts of each WTE are included in the

Example workflow of the toolbox MATLAB implementation. The example workflow is based on part of the provided Work-Through Example 2 (WTE2) that takes the user through the processing and analysis of particle trajectories inside an evaporating droplet [

A Python implementation of

The Python implementation is available on the Gitlab repository:

The MATLAB toolbox has been tested functionally on Windows 10 with MATLAB releases R2018b, 2020a, and 2020b, while the toolbox performance has been tested and investigated extensively in three recent publications [

In Barnkob and Rossi [

In Rossi and Barnkob [

In Barnkob et al. [_{S} (the higher, the more particle image overlapping). Depending on the particle image concentration and the achieved recall for the given _{m}-value, the depth coordinate uncertainties _{z}_{x}, σ_{y}

Example validation of the

Windows, UNIX/Linux, Macintosh (and any operating system supporting MATLAB).

MATLAB 9.4.0 (R2018a), upward compatible.

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The MATLAB implementation requires the additional MATLAB toolboxes: ‘curve_fitting_toolbox’, ‘image_toolbox’, ‘statistics_toolbox’

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English

The analysis of particle positions, velocities, and trajectories is an integral part of many research disciplines. This includes analyses in 2D as well as in 3D, and with the rapid growth in fields relying on microscopy, such single-camera methods can provide unique and important information. One example is within the field of microfluidics where high control of fluid flow and externally-applied forces is becoming an important tool in biomedical research and applications. Here, 3D detection and tracking of particles and cells can provide the necessary information needed for optimization, standardization, and real-time inspection and control [

GDPT has shown to be an excellent candidate for a wide-spread technique as is a simple and universal defocusing-based method and requires no special optics and can be used in standard microscope setups. Here, the development of free, accessible, user-friendly, and accurate tools can greatly enhance the practicability and availability of the method. One example is the MATLAB implementation GDPTlab (also by the authors), which has been distributed to researchers since 2015 and has proven its value in a number of research projects including work in journals such as Proceedings of the National Academy of Sciences, Physical Review Letters, and Scientific Reports [

All material, without exception, is available via the permanent repository:

We would like to thank all GDPTlab and

The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 713683 (COFUNDfellows-DTU).

The authors have no competing interests to declare.