Direct Inference of Cell Positions using Lens-Free Microscopy and Deep Learning

Philipp Grüning, Falk Nette, Noah Heldt, Ana Cristina Guerra de Souza, Erhardt Barth


With in-line holography, it is possible to record biological cells over time in a three-dimensional hydrogel without the need for staining, providing the capability of observing cell behavior in a minimally invasive manner. However, this setup currently requires computationally intensive image-reconstruction algorithms to determine the required cell statistics. In this work, we directly extract cell positions from the holographic data by using deep neural networks and thus avoid several reconstruction steps. We show that our method is capable of substantially decreasing the time needed to extract information from the raw data without loss in quality.
Original languageEnglish
Title of host publicationMedical Imaging with Deep Learning
Number of pages9
Publication date2021
Publication statusPublished - 2021

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems

DFG Research Classification Scheme

  • 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation


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