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Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation
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Researchers developed a synthetic image rendering approach to solve the annotation bottleneck in deep learning-based nanoparticle segmentation, generating labeled training data automatically to enable precise statistical characterization of nanoparticle size and shape distributions.
Abstract 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 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, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows 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, a segmentation accuracy can be derived that is comparable to man‐made annotations for toxicologically relevant metal‐oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high‐throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro‐ and nanoplastic particles in water and tissue samples.
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