Lin, Binbin ; Emami, Nima ; Santos, David A. ; Luo, Yuting ; Banerjee, Sarbajit ; Xu, Bai-Xiang (2022)
A deep learned nanowire segmentation model using synthetic data augmentation.
In: npj Computational Materials, 2022, 8
doi: 10.26083/tuprints-00021425
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Lin, Binbin ; Emami, Nima ; Santos, David A. ; Luo, Yuting ; Banerjee, Sarbajit ; Xu, Bai-Xiang |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | A deep learned nanowire segmentation model using synthetic data augmentation |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | npj Computational Materials |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| Kollation: | 12 Seiten |
| DOI: | 10.26083/tuprints-00021425 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21425 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung aus gefördertem Golden Open Access |
| Kurzbeschreibung (Abstract): | 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. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-214250 |
| Zusätzliche Informationen: | Keywords: Characterization and analytical techniques, imaging techniques, optical spectroscopy |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Mechanik Funktionaler Materialien |
| Hinterlegungsdatum: | 07 Jun 2022 12:14 |
| Letzte Änderung: | 08 Jun 2022 05:57 |
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| Suche nach Titel in: | TUfind oder in Google |
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- A deep learned nanowire segmentation model using synthetic data augmentation. (deposited 07 Jun 2022 12:14) [Gegenwärtig angezeigt]
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