Characterizing Ensembles of Platelike Particles via Machine Learning

The size and shape characterization of platelike particles using a dual projection imaging device is presented. Based on the published algorithm to estimate the three lengths of the particles from the image pairs (method 1), the average error on the shortest length is around 140% on a test set of particles with different sizes, shapes, and alignment. Therefore, two potential improvements are tested in a simulation setting. The first approach uses an additional projection coupled with an oriented bounding box to estimate the three lengths (method 2). The second approach uses a machine learning model to estimate the three lengths from two projections (method 3). It is shown that method 2 marginally increases the accuracy, while method 3 leads to a significant improvement with an average error of 33% for the shortest characteristic length of particles in the test set and even less for the other lengths. It is shown that this remaining error is caused by the particle alignment with respect to the cameras. Since platelets cannot always be automatically distinguished from quasi-equant particles due to alignment issues, the model was also trained to estimate the three characteristic lengths of quasi-equant particles. The training set and the test set, used for model training and validation, respectively, comprise particles that exhibit quasi-equant, needlelike, and platelike shapes. Several machine learning models are identified and optimized to predict the three particle lengths based on the two projections from the imaging device. Artificial neural networks were chosen because of their superior predictive performance.