  Nanoparticles occur in various environments as a consequence of man-made
processes, which raises concerns about their impact on the environment and
human health. To allow for proper risk assessment, a precise and statistically
relevant analysis of particle characteristics (such as e.g. size, shape and
composition) is required that would greatly benefit from automated image
analysis procedures. While deep learning shows impressive results in object
detection tasks, its applicability is limited by the amount of representative,
experimentally collected and manually annotated training data. Here, we present
an elegant, flexible and versatile method to bypass this costly and tedious
data acquisition process. We show that using a rendering software allows to
generate realistic, synthetic training data to train a state-of-the art deep
neural network. Using this approach, we derive a segmentation accuracy that is
comparable to man-made annotations for toxicologically relevant metal-oxide
nanoparticle ensembles which we chose as examples. Our study paves the way
towards the use of deep learning for automated, high-throughput particle
detection in a variety of imaging techniques such as microscopies and
spectroscopies, for a wide variety of studies and applications, including the
detection of plastic micro- and nanoparticles.
