Advancing Paired Image-Mask Synthesis for Automated Nanoparticle Phenotyping

Abstract

Paired image-mask synthesis is essential for automating nanoparticle phenotyping in drug research, as it creates large-scale annotated image datasets for effective learning. However, challenges of complex spatial and morphological variations, such as dense, sparse, and overlapping structures, hinder existing synthesis methods, which often fail to produce high-quality images and masks from limited data. To tackle these challenges, This work introduces a novel conditional Generative Adversarial Network tailored for high-quality image-mask synthesis under limited training conditions. The proposed model features a dual-branch generator that independently synthesizes images and masks while maintaining structural consistency. It also employs a diffusion-based multi-level discriminator to enhance image quality feedback across various noise levels and spatial scales. Qualitative and quantitative evaluations show that the proposed method enhances both synthetic image quality and mask accuracy. This advancement has the potential to streamline the synthesis of large-scale image-mask pairs for nanoparticles, ultimately benefiting automated phenotyping and drug screening efforts.