A deep learning solution for particle size analysis in low resolution inline microscopy images based on generative adversarial network

We introduce a new strategy for image analysis of inline microscopy monitoring estimate particle size distribution using deep learning. The proposed method consists of two major components: First, a novel way to generate training image-label pairs with a high-level of credibility via a Cycle-consistent Generative Adversarial Network (CycleGAN), and second, a Mask-RCNN model trained with the generated data for the particle detection task. The proposed methodology eliminates the need for manual labeling in the training phase which is a labor-intensive step and can result in labeling errors given the fuzziness of these images. We studied the application of this strategy to images acquired with a particle vision and measurement (PVM) probe. The proposed methodology was applied to images of two particle morphologies with different sizes and concentrations. Our results showed that the proposed methodology could be inexpensively used to determine qualitative trends between crystal size distributions. This trend information is a very important aspect of crystallization process monitoring and is often enough to determine what is controlling the crystallization. Therefore, we see our approach as a step in the right direction to provide insights into the particularly challenging PVM inline microscopy monitoring process without the need for offline sampling.