Image Synthesis and Modified BlendMask Instance Segmentation for Automated Nanoparticle Phenotyping

• Published in: IEEE transactions on medical imaging 2023-12, Vol.42 (12), p.1-1
• Main Authors: Tang, Xiaoqin, Lv, Lingpeng, Javanmardi, Shima, Wang, Yunfeng, Fan, Jingchuan, Verbeek, Fons J., Xiao, Guoqiang
• Format: Article
• Language: English
• Subjects: Automation; deep learning; Feature extraction; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Image synthesis; Instance segmentation; Microscope image synthesis; Microscopy; nanoparticle phenotyping; Nanoparticles; Phenotyping; Pipelines; Scanning electron microscopy; Synthesis; Task analysis
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2023.3299119

Automated nanoparticle phenotyping is a critical aspect of high-throughput drug research, which requires analyzing nanoparticle size, shape, and surface topography from microscopy images. To automate this process, we present an instance segmentation pipeline that partitions individual nanoparticles on microscopy images. Our pipeline makes two key contributions. Firstly, we synthesize diverse and approximately realistic nanoparticle images to improve robust learning. Secondly, we improve the BlendMask model to segment tiny, overlapping, or sparse particle images. Specifically, we propose a parameterized approach for generating novel pairs of single particles and their masks, encouraging greater diversity in the training data. To synthesize more realistic particle images, we explore three particle placement rules and an image selection criterion. The improved one-stage instance segmentation network extracts distinctive features of nanoparticles and their context at both local and global levels, which addresses the data challenges associated with tiny, overlapping, or sparse nanoparticles. Extensive experiments demonstrate the effectiveness of our pipeline for automating nanoparticle partitioning and phenotyping in drug research using microscopy images.