BeadNet: deep learning-based bead detection and counting in low-resolution microscopy images

MOTIVATION: An automated counting of beads is required for many high-throughput experiments such as studying mimicked bacterial invasion processes. However, state-of-the-art algorithms under- or overestimate the number of beads in low-resolution images. In addition, expert knowledge is needed to adj...

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Detalles Bibliográficos
Autores principales: Scherr, Tim, Streule, Karolin, Bartschat, Andreas, Böhland, Moritz, Stegmaier, Johannes, Reischl, Markus, Orian-Rousseau, Véronique, Mikut, Ralf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750944/
https://www.ncbi.nlm.nih.gov/pubmed/32589734
http://dx.doi.org/10.1093/bioinformatics/btaa594

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750944/
https://www.ncbi.nlm.nih.gov/pubmed/32589734
http://dx.doi.org/10.1093/bioinformatics/btaa594

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