Cargando…
Automatic improvement of deep learning-based cell segmentation in time-lapse microscopy by neural architecture search
MOTIVATION: Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and complicated temporal behaviors. In recent years, numerous deep learning-based methods have been...
Autores principales: | Zhu, Yanming, Meijering, Erik |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665766/ https://www.ncbi.nlm.nih.gov/pubmed/34329376 http://dx.doi.org/10.1093/bioinformatics/btab556 |
Ejemplares similares
-
A comparison of manual and automated neural architecture search for white matter tract segmentation
por: Tchetchenian, Ari, et al.
Publicado: (2023) -
DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy
por: Zargari, Abolfazl, et al.
Publicado: (2023) -
Network analysis of time-lapse microscopy recordings
por: Smedler, Erik, et al.
Publicado: (2014) -
Neural network control of focal position during time-lapse microscopy of cells
por: Wei, Ling, et al.
Publicado: (2018) -
Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes
por: Franco-Barranco, Daniel, et al.
Publicado: (2021)