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.

deepBlink: threshold-independent detection and localization of diffraction-limited spots / B.T. Eichenberger, Y. Zhan, M. Rempfler, L. Giorgetti, J. Chao, A. - In: NUCLEIC ACIDS RESEARCH. - ISSN 0305-1048. - 49:13(2021), pp. 7292-7297. [10.1093/nar/gkab546]

deepBlink: threshold-independent detection and localization of diffraction-limited spots

Y. Zhan;L. Giorgetti;
2021

Abstract

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.
Settore BIOS-08/A - Biologia molecolare
   Biophysical mechanisms of long-range transcriptional regulation
   BioMeTRe
   European Commission
   Horizon 2020 Framework Programme
   759366
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1156658
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