LayoutDualGAN: Advancing Automated Drug Nanoparticle Phenotyping via Enhanced Pairwise Image-Mask Synthesis

Automated nanoparticle phenotyping in drug discovery requires robust learning models trained on large, annotated datasets of microscope images. However, obtaining detailed pixel-level annotations for large-scale biomedical images is a challenging and labor-intensive task. Image synthesis offers a potential solution to augment limited training datasets through the generation of paired image-mask samples. While existing synthesis methods prioritize image fidelity, they often overlook the accuracy of the corresponding masks in terms of structural consistency with the generated images. To address this gap, we propose LayoutDualGAN, a layout-conditioned GAN that synthesizes both high-fidelity nanoparticle images and structurally more consistent masks. Our approach features a dual-path generator comprising an instance-level mask generator and a mask-aware image generator. It enforces a tight coupling between generated images and their corresponding masks, guaranteeing enhanced image-mask consistency. Additionally, we design a robust multilevel discriminator that boosts discrimination feedback across image and instance levels, ensuring approximately realistic image synthesis. Experimental results demonstrate that the proposed model, trained on an extremely small dataset, outperforms existing methods in terms of both image quality and mask accuracy for the task of annotated nanoparticle image generation.