Automated particle segmentation and feature analysis of experimental image data are indispensable for data-driven material science.Deep learning-based image segmentation algorithms are promising techniques to achieve ...Automated particle segmentation and feature analysis of experimental image data are indispensable for data-driven material science.Deep learning-based image segmentation algorithms are promising techniques to achieve this goal but are challenging to use due to the acquisition of a large number of training images.In the present work,synthetic images are applied,resembling the experimental images in terms of geometrical and visual features,to train the state-of-art Mask region-based convolutional neural networks to segment vanadium pentoxide nanowires,a cathode material within optical density-based images acquired using spectromicroscopy.The results demonstrate the instance segmentation power in real optical intensity-based spectromicroscopy images of complex nanowires in overlapped networks and provide reliable statistical information.The model can further be used to segment nanowires in scanning electron microscopy images,which are fundamentally different from the training dataset known to the model.The proposed methodology can be extended to any optical intensity-based images of variable particle morphology,material class,and beyond.展开更多
基金This work is supported by German Research Foundation(DFG)B.L.and B.-X.X.acknowledge the financial support under the grant agreement No.405422877 of the Paper Research project(FiPRe)and the Federal Ministry of Education and Research(BMBF)and the state of Hesse as part of the NHR ProgramThe calculations for this research were conducted with computing resources under the project project1020,special0007The research at Texas A&M University was supported by the NSF under DMR 1627197.D.A.S.acknowledges support under a NSF Graduate Research Fellowship under grant No.1746932.
文摘Automated particle segmentation and feature analysis of experimental image data are indispensable for data-driven material science.Deep learning-based image segmentation algorithms are promising techniques to achieve this goal but are challenging to use due to the acquisition of a large number of training images.In the present work,synthetic images are applied,resembling the experimental images in terms of geometrical and visual features,to train the state-of-art Mask region-based convolutional neural networks to segment vanadium pentoxide nanowires,a cathode material within optical density-based images acquired using spectromicroscopy.The results demonstrate the instance segmentation power in real optical intensity-based spectromicroscopy images of complex nanowires in overlapped networks and provide reliable statistical information.The model can further be used to segment nanowires in scanning electron microscopy images,which are fundamentally different from the training dataset known to the model.The proposed methodology can be extended to any optical intensity-based images of variable particle morphology,material class,and beyond.
