Deep learning-enhanced light-field imaging with continuous validation

Research output: Contribution to journalArticle

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.

Details

Authors
  • Nils Wagner
  • Fynn Beuttenmueller
  • Nils Norlin
  • Jakob Gierten
  • Joachim Wittbrodt
  • Martin Weigert
  • Lars Hufnagel
  • Robert Prevedel
  • Anna Kreshuk
Organisations
External organisations
  • European Molecular Biology Laboratory Heidelberg
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Cell and Molecular Biology
Original languageEnglish
Article number2020.07.30.228924
Number of pages24
JournalbioRxiv
Publication statusPublished - 2020 Jul 31
Publication categoryResearch
Peer-reviewedNo