DiffRenderGAN：通过可微分渲染和生成建模解决定量纳米材料分析深度分割网络中的训练数据稀缺问题
DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling
AI总结：
针对定量纳米材料分析中深度分割网络训练数据稀缺问题，提出集成可微分渲染器的DiffRenderGAN生成模型，从无标注真实显微图像生成带标注合成数据，提升分割性能并弥合合成与真实数据差距。
摘要：
纳米材料表现出由尺寸、形状和表面特性等参数决定的独特性质，这些参数在技术、生物和环境背景下对其应用和相互作用具有关键影响。准确量化和理解这些材料对于推进研究和创新至关重要。在这方面，深度学习分割网络已成为强大工具，可实现自动化洞察，并以精确的定量分析取代主观方法。然而，其效能依赖于具有代表性的标注数据集，而由于纳米颗粒成像成本高昂以及手动标注的劳动密集型性质，这类数据集难以获取。为克服这些限制，我们提出DiffRenderGAN，一种旨在生成带标注合成数据的新型生成模型。通过将可微分渲染器集成到生成对抗网络（GAN）框架中，DiffRenderGAN优化纹理渲染参数，从无标注的真实显微图像生成逼真的带标注纳米颗粒图像。与现有合成数据方法相比，该方法通过生成多样化且逼真的数据，减少了对人工干预的需求并提升了分割性能。在包括二氧化钛（TiO₂）、二氧化硅（SiO₂）和银纳米线（AgNW）在内的多个离子和电子显微镜案例中进行测试后，DiffRenderGAN弥合了合成数据与真实数据之间的差距，推进了对复杂纳米材料系统的量化和理解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
