Deep learning-enhanced light-field imaging with continuous validation

Research output: Contribution to journalArticle

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.

Details

Authors
  • Nils Wagner
  • Fynn Beuttenmueller
  • Nils Norlin
  • Jakob Gierten
  • Juan Carlos Boffi
  • Joachim Wittbrodt
  • Martin Weigert
  • Lars Hufnagel
  • Robert Prevedel
  • Anna Kreshuk
Organisations
External organisations
  • European Molecular Biology Laboratory Heidelberg
  • University Hospital Heidelberg
  • Heidelberg University
  • Swiss Federal Institute of Technology
  • European Molecular Biology Laboratory (EMBL Rome)
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Radiology, Nuclear Medicine and Medical Imaging
Original languageEnglish
Pages (from-to)557-563
JournalNature Methods
Volume18
Issue number5
Publication statusPublished - 2021 May
Publication categoryResearch
Peer-reviewedYes

Related research output

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

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