Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling
Abstract Nanomaterials’ properties, influenced by size, shape, and surface characteristics, are crucial for their technological, biological, and environmental applications. Accurate quantification of these materials is essential for advancing research. Deep learning segmentation networks offer preci...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01702-6 |
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| _version_ | 1849334438527238144 |
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| author | Dennis Possart Leonid Mill Florian Vollnhals Tor Hildebrand Peter Suter Mathis Hoffmann Jonas Utz Daniel Augsburger Mareike Thies Mingxuan Gu Fabian Wagner George Sarau Silke Christiansen Katharina Breininger |
| author_facet | Dennis Possart Leonid Mill Florian Vollnhals Tor Hildebrand Peter Suter Mathis Hoffmann Jonas Utz Daniel Augsburger Mareike Thies Mingxuan Gu Fabian Wagner George Sarau Silke Christiansen Katharina Breininger |
| author_sort | Dennis Possart |
| collection | DOAJ |
| description | Abstract Nanomaterials’ properties, influenced by size, shape, and surface characteristics, are crucial for their technological, biological, and environmental applications. Accurate quantification of these materials is essential for advancing research. Deep learning segmentation networks offer precise, automated analysis, but their effectiveness depends on representative annotated datasets, which are difficult to obtain due to the high cost and manual effort required for imaging and annotation. To address this, we present DiffRenderGAN, a generative model that produces annotated synthetic data by integrating a differentiable renderer into a Generative Adversarial Network (GAN) framework. DiffRenderGAN optimizes rendering parameters to produce realistic, annotated images from non-annotated real microscopy images, reducing manual effort and improving segmentation performance compared to existing methods. Tested on ion and electron microscopy datasets, including titanium dioxide (TiO2), silicon dioxide (SiO2), and silver nanowires (AgNW), DiffRenderGAN bridges the gap between synthetic and real data, advancing the quantification and understanding of complex nanomaterial systems. |
| format | Article |
| id | doaj-art-04b9fddfdffc487dbbeb75862873bbed |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-04b9fddfdffc487dbbeb75862873bbed2025-08-20T03:45:34ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111110.1038/s41524-025-01702-6Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modelingDennis Possart0Leonid Mill1Florian Vollnhals2Tor Hildebrand3Peter Suter4Mathis Hoffmann5Jonas Utz6Daniel Augsburger7Mareike Thies8Mingxuan Gu9Fabian Wagner10George Sarau11Silke Christiansen12Katharina Breininger13Correlative Microscopy and Materials Data, Fraunhofer Institute for Ceramic Technologies and SystemsPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergInstitute for Nanotechnology and Correlative MicroscopyLucid Concepts AGLucid Concepts AGPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergDepartment Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-NürnbergCorrelative Microscopy and Materials Data, Fraunhofer Institute for Ceramic Technologies and SystemsPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergCorrelative Microscopy and Materials Data, Fraunhofer Institute for Ceramic Technologies and SystemsCorrelative Microscopy and Materials Data, Fraunhofer Institute for Ceramic Technologies and SystemsDepartment Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-NürnbergAbstract Nanomaterials’ properties, influenced by size, shape, and surface characteristics, are crucial for their technological, biological, and environmental applications. Accurate quantification of these materials is essential for advancing research. Deep learning segmentation networks offer precise, automated analysis, but their effectiveness depends on representative annotated datasets, which are difficult to obtain due to the high cost and manual effort required for imaging and annotation. To address this, we present DiffRenderGAN, a generative model that produces annotated synthetic data by integrating a differentiable renderer into a Generative Adversarial Network (GAN) framework. DiffRenderGAN optimizes rendering parameters to produce realistic, annotated images from non-annotated real microscopy images, reducing manual effort and improving segmentation performance compared to existing methods. Tested on ion and electron microscopy datasets, including titanium dioxide (TiO2), silicon dioxide (SiO2), and silver nanowires (AgNW), DiffRenderGAN bridges the gap between synthetic and real data, advancing the quantification and understanding of complex nanomaterial systems.https://doi.org/10.1038/s41524-025-01702-6 |
| spellingShingle | Dennis Possart Leonid Mill Florian Vollnhals Tor Hildebrand Peter Suter Mathis Hoffmann Jonas Utz Daniel Augsburger Mareike Thies Mingxuan Gu Fabian Wagner George Sarau Silke Christiansen Katharina Breininger Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling npj Computational Materials |
| title | Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling |
| title_full | Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling |
| title_fullStr | Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling |
| title_full_unstemmed | Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling |
| title_short | Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling |
| title_sort | addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling |
| url | https://doi.org/10.1038/s41524-025-01702-6 |
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