Abstract
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 language | English |
|---|---|
| Title of host publication | Medical Imaging with Deep Learning |
| Number of pages | 9 |
| Publication date | 2021 |
| Pages | 219-227 |
| Publication status | Published - 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being -
SDG 4 Quality Education -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 11 Sustainable Cities and Communities -
SDG 12 Responsible Consumption and Production -
SDG 14 Life Below Water -
SDG 15 Life on Land
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
- Research Area: Intelligent Systems
DFG Research Classification Scheme
- 4.43-05 Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
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