Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning
Anno di pubblicazione2021
FormatoElettronico
Autore/iWen Xiao, Lu Xin, Runyu Cao, Xintong Wu, Ran Tian, Leiping Che, Lian-Wen Sun, Pietro Ferraro, Feng Pan
Affiliazioni autoriKey Laboratory of Precision Opto-mechatronics Technology, School of Instrumentation & Optoelectronic Engineering, Beihang University, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, CNR, Institute of Applied Sciences & Intelligent Systems (ISASI) "E. Caianiello",
Autori CNR e affiliazioni
  • PIETRO FERRARO
Lingua/e
  • inglese
AbstractWe present sensing time-lapse morphogenesis of living bone cells under micro-fluidic shear stress (FSS) by digital holographic (DH) microscopy. To remove the effect of aberrations on quantitative measurements, we propose a numerical and automatic method to compensate for aberrations based on a convolutional neural network (CNN). For the first time, the aberration compensation issue is considered as a regression task where optimal coefficients for constructing the phase aberration map act as responses corresponding to the input aberrated phase image. We adopted tens of thousands of living cells' phase images reconstructed from digital holograms for training the CNN. The experiments demonstrate that, based on the trained network, phase aberrations can be totally removed in real-time without any hypothesis of object and aberration phase, knowledge of the setup's physical parameters, and the operation of selecting background regions; hence, the morphogenesis of the bone cells under FSS is accurately detected and quantitatively analyzed. The results show that the proposed method could provide a highly efficient and versatile way to investigate the effects of micro-FSS on living biological cells in microfluidic lab-on-chip platforms thanks to the combination of phase-contrast label-free microcopy with artificial intelligence.
Lingua abstractinglese
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RivistaLab on a chip (Print)
Attiva dal 2001
Editore: Royal Society of Chemistry, - Cambridge
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1473-0197
Titolo chiave: Lab on a chip (Print)
Titolo proprio: Lab on a chip (Print)
Titolo abbreviato: Lab chip (Print)
Numero volume della rivista-
Fascicolo della rivista-
DOI10.1039/D0LC01113D
Verificato da refereeSì: Internazionale
Stato della pubblicazionePreprint
Indicizzazione (in banche dati controllate)-
Parole chiaveigital holographic microscopy, deep learning, microfluidic, cells
Link (URL, URI)https://pubs.rsc.org/en/content/articlehtml/2021/lc/d0lc01113d
Titolo parallelo-
LicenzaCreative commons
Scadenza embargo-
Data di accettazione08/02/2021
Note/Altre informazioni-
Strutture CNR
  • ISASI — Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello"
Moduli/Attività/Sottoprogetti CNR
  • DFM.AD004.238.001 : PRIN 2017-2017N7R2CJ_001-Pietro Ferraro MORphological biomarkers For Early diagnosis in Oncology - MORFEO
Progetti Europei-
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