deepBlink: threshold-independent detection and localization of diffraction-limited spots.
Bastian Th EichenbergerYinXiu ZhanMarkus RempflerLuca GiorgettiJeffrey A ChaoPublished in: Nucleic acids research (2021)
Detection of diffraction-limited spots in single-molecule microscopy images is traditionally performed with mathematical operators designed for idealized spots. This process requires manual tuning of parameters that is time-consuming and not always reliable. We have developed deepBlink, a neural network-based method to detect and localize spots automatically. We demonstrate that deepBlink outperforms other state-of-the-art methods across six publicly available datasets containing synthetic and experimental data.
Keyphrases
- single molecule
- neural network
- label free
- living cells
- atomic force microscopy
- loop mediated isothermal amplification
- real time pcr
- optical coherence tomography
- deep learning
- high resolution
- electronic health record
- high throughput
- machine learning
- rna seq
- big data
- crystal structure
- artificial intelligence
- mass spectrometry