Deep learning-enhanced light-field imaging with continuous validation

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

Light field microscopy (LFM) has emerged as a powerful tool for fast volumetric image acquisition in biology, but its effective throughput and widespread use has been hampered by a computationally demanding and artefact-prone image reconstruction process. Here, we present a novel framework consisting of a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction, where single light-sheet acquisitions continuously serve as training data and validation for the convolutional neural network reconstructing the LFM volume. Our network delivers high-quality reconstructions at video-rate throughput and we demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity.

Detaljer

Författare
  • Nils Wagner
  • Fynn Beuttenmueller
  • Nils Norlin
  • Jakob Gierten
  • Joachim Wittbrodt
  • Martin Weigert
  • Lars Hufnagel
  • Robert Prevedel
  • Anna Kreshuk
Enheter & grupper
Externa organisationer
  • European Molecular Biology Laboratory Heidelberg
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Cell- och molekylärbiologi
Originalspråkengelska
Artikelnummer2020.07.30.228924
Antal sidor24
TidskriftbioRxiv
StatusPublished - 2020 jul 31
PublikationskategoriForskning
Peer review utfördNej