DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912700033925120 |
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| author | Possart, Dennis Mill, Leonid Vollnhals, Florian Hildebrand, Tor Suter, Peter Hoffmann, Mathis Utz, Jonas Augsburger, Daniel Thies, Mareike Wu, Mingxuan Wagner, Fabian Sarau, George Christiansen, Silke Breininger, Katharina |
| author_facet | Possart, Dennis Mill, Leonid Vollnhals, Florian Hildebrand, Tor Suter, Peter Hoffmann, Mathis Utz, Jonas Augsburger, Daniel Thies, Mareike Wu, Mingxuan Wagner, Fabian Sarau, George Christiansen, Silke Breininger, Katharina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling Possart, Dennis Mill, Leonid Vollnhals, Florian Hildebrand, Tor Suter, Peter Hoffmann, Mathis Utz, Jonas Augsburger, Daniel Thies, Mareike Wu, Mingxuan Wagner, Fabian Sarau, George Christiansen, Silke Breininger, Katharina Materials Science Computer Vision and Pattern Recognition Machine Learning 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. |
| title | DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling |
| topic | Materials Science Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2502.09477 |