Deep learning-enhanced light-field imaging with continuous validation

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence-enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz.

Detaljer

Författare
  • Nils Wagner
  • Fynn Beuttenmueller
  • Nils Norlin
  • Jakob Gierten
  • Juan Carlos Boffi
  • Joachim Wittbrodt
  • Martin Weigert
  • Lars Hufnagel
  • Robert Prevedel
  • Anna Kreshuk
Enheter & grupper
Externa organisationer
  • European Molecular Biology Laboratory Heidelberg
  • University Hospital Heidelberg
  • Heidelberg University
  • Swiss Federal Institute of Technology
  • EMBL Mouse Biology Program
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Radiologi och bildbehandling
Originalspråkengelska
Sidor (från-till)557-563
TidskriftNature Methods
Volym18
Utgåva nummer5
StatusPublished - 2021 maj
PublikationskategoriForskning
Peer review utfördJa

Relaterad forskningsoutput

Nils Wagner, Fynn Beuttenmueller, Nils Norlin, Jakob Gierten, Joachim Wittbrodt, Martin Weigert, Lars Hufnagel, Robert Prevedel & Anna Kreshuk, 2020 jul 31, (Submitted) 24 s.

Forskningsoutput: Övriga bidragÖvrigt

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