EconPapers    
Economics at your fingertips  
 

Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network

Jihwan Kim, Youngdo Kim, Hyo Seung Lee, Eunseok Seo and Sang Joon Lee ()
Additional contact information
Jihwan Kim: Pohang University of Science and Technology
Youngdo Kim: Pohang University of Science and Technology
Hyo Seung Lee: Pohang University of Science and Technology
Eunseok Seo: Sogang University
Sang Joon Lee: Pohang University of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Recent advances in deep learning-based image reconstruction techniques have led to significant progress in phase retrieval using digital in-line holographic microscopy (DIHM). However, existing phase retrieval methods have technical limitations in 3D morphology reconstruction from single-shot holograms of biological cells. In this study, we propose a deep learning model, named MorpHoloNet, for single-shot reconstruction of 3D morphology by integrating physics-driven and coordinate-based neural networks. By simulating optical diffraction of coherent light through a 3D phase shift distribution, MorpHoloNet is optimized by minimizing the loss between simulated and input holograms on the detector plane. MorpHoloNet enables direct reconstruction of 3D complex light field and 3D morphology of a test sample from its single-shot hologram without requiring multiple phase-shifted holograms or angular scanning. It would be utilized to reconstruct spatiotemporal variations in 3D translational and rotational behaviors, as well as morphological deformations of biological cells from consecutive single-shot holograms captured using DIHM.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-60200-x Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60200-x

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-60200-x

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-05-27
Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60200-x