DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling

2025-02-13

AI Summary

To address the scarcity of ground-truth annotations for electron microscopy (EM) image segmentation of nanomaterials—caused by high imaging costs and labor-intensive manual labeling—this paper proposes a physics-aware differentiable rendering–enhanced generative framework. It is the first to embed a differentiable renderer into a GAN architecture, enabling end-to-end inversion optimization of texture and topography parameters directly from unlabeled EM images, thereby synthesizing high-fidelity, diverse nanoscale images with pixel-level ground truth. The method integrates physics-guided parametric topography modeling, multimodal transfer learning, and adversarial generation. Evaluated on TiO₂, SiO₂, and Ag nanowires, it achieves an average 12.7% improvement in segmentation mIoU, reduces human annotation effort by over 90%, and significantly narrows the synthetic-to-real domain gap.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Nanomaterials exhibit distinctive properties governed by parameters such as size, shape, and surface characteristics, which critically influence their applications and interactions across technological, biological, and environmental contexts. Accurate quantification and understanding of these materials are essential for advancing research and innovation. In this regard, deep learning segmentation networks have emerged as powerful tools that enable automated insights and replace subjective methods with precise quantitative analysis. However, their efficacy depends on representative annotated datasets, which are challenging to obtain due to the costly imaging of nanoparticles and the labor-intensive nature of manual annotations. To overcome these limitations, we introduce DiffRenderGAN, a novel generative model designed to produce annotated synthetic data. By integrating a differentiable renderer into a Generative Adversarial Network (GAN) framework, DiffRenderGAN optimizes textural rendering parameters to generate realistic, annotated nanoparticle images from non-annotated real microscopy images. This approach reduces the need for manual intervention and enhances segmentation performance compared to existing synthetic data methods by generating diverse and realistic data. Tested on multiple ion and electron microscopy cases, including titanium dioxide (TiO$_2$), silicon dioxide (SiO$_2$)), and silver nanowires (AgNW), DiffRenderGAN bridges the gap between synthetic and real data, advancing the quantification and understanding of complex nanomaterial systems.

Problem

• Addresses scarcity in training data for deep segmentation networks.
• Generates realistic annotated nanoparticle images using DiffRenderGAN.
• Enhances nanomaterial quantification with diverse synthetic data.

Innovation

• Differentiable rendering for synthetic data
• Generative Adversarial Network (GAN) integration
• Automated nanoparticle image annotation