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- Title
Front Cover: Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation (Small Methods 7/2021).
- Authors
Mill, Leonid; Wolff, David; Gerrits, Nele; Philipp, Patrick; Kling, Lasse; Vollnhals, Florian; Ignatenko, Andrew; Jaremenko, Christian; Huang, Yixing; De Castro, Olivier; Audinot, Jean‐Nicolas; Nelissen, Inge; Wirtz, Tom; Maier, Andreas; Christiansen, Silke
- Abstract
Helium ion microscopy, image analysis, machine learning, nanoparticles, segmentation, toxicology Keywords: helium ion microscopy; image analysis; machine learning; nanoparticles; segmentation; toxicology EN helium ion microscopy image analysis machine learning nanoparticles segmentation toxicology 1 1 1 07/16/21 20210701 NES 210701 In article number 2100223, Mill and co-workers demonstrated a novel methodology that tackles the data annotation problem for the deep learning-based segmentation of complex nanoparticle agglomerates in helium ion microscopy images.
- Subjects
PROBLEM solving; FIELD ion microscopy; DEEP learning; NANOPARTICLE toxicity; HELIUM ions; IMAGE segmentation; RENDERING (Computer graphics); IMAGE analysis
- Publication
Small Methods, 2021, Vol 5, Issue 7, p1
- ISSN
2366-9608
- Publication type
Article
- DOI
10.1002/smtd.202170028