Maximizing data extraction from colloidal particle micrographs through artificial intelligence-based image segmentation

Abstract

A detailed morphological analysis of colloidal particles from micrographs is a process that necessitates the identification and measurement of numerous features. The automation of image processing while maintaining a high level of accuracy is imperative for the advancement of colloid and materials science. Automated workflows should enable the analysis of large datasets, thereby enhancing the statistical significance and reliability of particle characterization. Pattern recognition and image segmentation are key in isolating features within micrographs, thereby enabling their subsequent classification. The process of semantic segmentation organizes pixel regions into meaningful classes, thereby distinguishing particles from the background and enabling the differentiation between different types of particles. The advent of artificial intelligence (AI), particularly through machine learning (ML), neural networks (NN), and deep learning (DL) is currently changing the field of microscopy analysis and enhances analytical capabilities in image analysis. This is accomplished by enabling adaptive and accurate decision-making during data processing. The Segment Anything Model (SAM) from MetaAI allows one to study large collections of nanoparticles without additional manual labor. This allows for rapid processing and analysis. Regarding complex particles composed of individual domains, the SAM model automates the segmentation of nanoparticles into distinct groups, enabling the identification of specific particle types. In the course of this development, it is to be expected that the analysis of colloidal particles is becoming more precise, efficient, and robust. This, in turn, is expected to stimulate innovation in diverse areas, including microscopy, colloid science, materials research, and other related disciplines.