Automated Nanoparticle Count via Modified BlendMask Instance Segmentation on SEM Images

Abstract

In the high-throughput drug research, statistical analysis of nanoparticles has been one of the focuses in drug carrier systems. This can be completed via electronic microscopy imaging and image analysis such as image segmentation. For example, in some cases selecting and counting the nanoparticles in the field of view are important for drug screening. In order to minimize manual interactions and avoid extensive workloads, we present a pipeline that is featured by deep learning-based instance segmentation, with experiments implemented on both real and synthetic data. The proposed instance segmentation approach, namely Modified BlendMask, aims to improve the accuracy of nanoparticle detection and further refine the automated nanoparticle count. In this framework, we are devoted to address the problem of missing detection, i.e., false negative, introduced by overlap and blur, sparse particle distribution, and tiny particle occurrence in images. Sufficient experiments demonstrate the reasonableness and effectiveness of the proposed pipeline and instance segmentation method for automated nanoparticle count, with an overall count accuracy of 70.2% for 38670 particles on the 1141 test images.