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 ...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(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.展开更多
基金supported by the European Union’s H2020 research and innovation program under the Marie Sklodowska-Curie grant agreement AIMed ID:861138D.P.,D.A.,G.S.and S.C.acknowledge the financial support from the European Union within the research projects 4D+nanoSCOPE ID:810316,LRI ID:C10,STOP ID:101057961+1 种基金from the German Research Foundation(DFG)within the research project UNPLOK ID:523847126,and from the“Freistaat Bayern”European Union within the project Analytiktechnikum für Gesundheits-und Umweltforschung AGEUM,StMWi-43-6623-22/1/3.
文摘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(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.
