Filtern
Erscheinungsjahr
- 2021 (2) (entfernen)
Dokumenttyp
- Vortrag (1)
- Forschungsdatensatz (1)
Sprache
- Englisch (2)
Referierte Publikation
- nein (2) (entfernen)
Schlagworte
- Artificial Intelligence (1)
- Automated Image Analysis (1)
- Automated image analysis (1)
- Electron Microscopy (1)
- Electron microscopy (1)
- Image Segmentation (1)
- Image segmentation (1)
- Neural Networks (1)
- Neural networks (1)
Organisationseinheit der BAM
- 1.2 Biophotonik (2) (entfernen)
Eingeladener Vortrag
- nein (1)
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.
This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6):
B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6
It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation