Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction

Ipek Anil Atalay Appak; Haobijam Johnson Singh; Sanna Korpela; Teemu O. Ihalainen; Erdem Sahin; Christine Guillemot; Humeyra Caglayan

doi:10.1038/s41377-026-02337-y

Your Location：

Home >

Browse articles >

Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction

Original Articles

- Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction
- Light: Science & Applications Vol. 15, Issue 8, Pages: 2720-2734(2026)
- Affiliations：
  
  1.Physics Unit, Faculty of Engineering and Natural Sciences, Tampere University, Tampere 33720, Finland
  2.INRIA Rennes, Bretagne Atlantique Research Centre, Rennes 35042, France
  3.Bio Unit, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
  4.Signal Processing Research Centre, Tampere University, Tampere 33720, Finland
  5.Department of Electrical Engineering, Photonic Integration Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
- Author bio：
  
  Humeyra Caglayan (h.caglayan@tue.nl)
- Funds：
- DOI：10.1038/s41377-026-02337-y
  CLC：
- Received：04 December 2025，
  
  Revised：2026-04-12，
  
  Accepted：28 April 2026，
  
  Online First：19 May 2026，
  
  Published：31 August 2026
- Accepted：
Scan QR Code
Atalay Appak, I. A. et al. Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction. Light: Science & Applications, 15, 2720-2734 (2026).
DOI：

Atalay Appak, I. A. et al. Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction. Light: Science & Applications, 15, 2720-2734 (2026). DOI： 10.1038/s41377-026-02337-y.

摘要

Abstract

High numerical aperture fluorescence microscopy provides subcellular resolution

but its depth of field is extremely limited

so thick specimens quickly fall out of focus and typically require axial scanning. Multispectral imaging further compoun

ds this problem because chromatic aberrations shift the best focus plane and distort registration across emission channels

especially in thick samples. In this work

we present MANTIS (Multispectral All-Depth meta-opTic Imaging System)

a co-designed computational microscopy imaging system that achieves extended depth-of-field from a single acquisition without axial scanning. MANTIS combines a learned meta-optic with a physics-guided neural network trained end-to-end to reconstruct sharp multispectral images from depth

and wavelength-dependent blurred sensor measurements. We experimentally demonstrate a 50

m extended depth of field at NA 1.1

corresponding to an 82-fold increase over a conventional wide-field microscope. In addition

simulations show that MANTIS can target different depth-of-field ranges

with the expected trade-off that larger depth-of-field ranges come at a cost in reconstruction fidelity. We validate the approach on biologically relevant fluorescence specimens

including 50

m thick three-dimensional cultured MDCK Ⅱ spheroids

and show that

compared with conventional wide-field fluorescence microscopy

reconstructions maintain contrast and lateral detail across depth

with reduced defocus blur and consistent performance across spectral channels.

关键词

Keywords

references

Liu, S. & Hua, H. Extended depth-of-field microscopic imaging with a variable focus microscope objective. Opt. Express 19 , 353–362 (2011)..

Glaser, A. K. et al. Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat. Biomed. Eng. 1 , 0084 (2017)..

Power, R. M. & Huisken, J. A guide to light-sheet fluorescence microscopy for multiscale imaging. Nat. Methods 14 , 360–373 (2017)..

Böhm, U. L. & Judkewitz, B. Fast and light-efficient remote focusing for volumetric voltage imaging. Nat. Commun. 15 , 9555 (2024)..

Prince, N. H., Sain, N. & Chakraborty, T. Remote refocusing for multi-scale imaging. J. Biomed. Opt. 29 , 080501 (2024)..

Botcherby, E. J. et al. Aberration-free optical refocusing in high numerical aperture microscopy. Opt. Lett. 32 , 2007–2009 (2007)..

Broxto n, M. et al. Wave optics theory and 3-D deconvolution for the light field microscope. Opt. Express 21 , 25418–25439 (2013)..

Gregor, I. et al. Instant three-color multiplane fluorescence microscopy. Biophys. Rep. 1 , 100001 (2021)..

Wang, P. et al. A single-shot hyperspectral phasor camera for fast, multi-color fluorescence microscopy. Cell Rep. Methods 3 , 100441 (2023)..

Zhang, C. S. et al. Deep tissue super-resolution imaging with adaptive optical two-photon multifocal structured illumination microscopy. PhotoniX 4 , 38 (2023)..

Icha, J. et al. Phototoxicity in live fluorescence microscopy, and how to avoid it. BioEssays 39 , 1700003 (2017)..

Dowski Jr, E. R. & Cathey, W. T. Extended depth of field through wave-front coding. Appl. Opt. 34 , 1859–1866 (1995)..

Cathey, W. T. & Dowski, E. R. New paradigm for imaging systems. Appl. Opt. 41 , 6080–6092 (2002)..

Akpinar, U. et al. Learning wavefront coding for extended depth of field imaging. IEEE Trans. Image Process. 30 , 3307–3320 (2021)..

Sitzmann, V. et al. End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graph. 37 , 114 (2018)..

Wu, Y. C. et al. PhaseCam3D—learning phase masks for passive single view depth estimation. In Proc. 2019 IEEE International Conference on Computational Photography (ICCP) 1–12 (IEEE, 2019).

Jin, L. B. et al. Deep learning extended depth-of-field microscope for fast and slide-free histology. Proc. Natl. Acad. Sci. USA 117 , 33051–33060 (2020)..

Sahin, E. et al. Learning extended depth of field hyperspectral imaging. In Proc. 2023 IEEE International Conference on Image Processing (ICIP) 1850–1854 (IEEE, 2023).

Yu, N. F. & Capasso, F. Flat optics with designer metasurfaces. Nat. Mater. 13 , 139–150 (2014)..

Khorasaninejad, M. et al. Metalenses at visible wavelengths: diffraction-limited focusing and subw avelength resolution imaging. Science 352 , 1190–1194 (2016)..

Huang, L. C. et al. Design and analysis of extended depth of focus metalenses for achromatic computational imaging. Photonics Res. 8 , 1613–1623 (2020)..

Whitehead, J. E. M. et al. Fast extended depth of focus meta-optics for varifocal functionality. Photonics Res. 10 , 828–833 (2022)..

Xiao, S. Y. et al. Inverse design of a near-infrared metalens with an extended depth of focus based on double-process genetic algorithm optimization. Opt. Express 31 , 8668–8681 (2023)..

Colburn, S., Zhan, A. L. & Majumdar, A. Metasurface optics for full-color computational imaging. Sci. Adv. 4 , 2114 (2018)..

Bayati, E. et al. Inverse designed extended depth of focus meta-optics for broadband imaging in the visible. Nanophotonics 11 , 2531–2540 (2022)..

Lin, R. J. et al. Achromatic metalens array for full-colour light-field imaging. Nat. Nanotechnol. 14 , 227–231 (2019)..

Fan, Z. B. et al. A broadband achromatic metalens array for integral imaging in the visible. Light Sci. Appl. 8 , 67 (2019)..

Xu, B. B. et al. Metalens-integrated compact imaging devices for wide-field microscopy. Adv. Photonics 2 , 066004 (2020)..

Seong, B. et al. E2E-BPF microscope: extended depth-of-field microscopy using learning-based implementation of binary phase filter and image deconvolution. Light Sci. Appl. 12 , 269 (2023)..

Jin, L. B. et al. DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology. Nat. Commun. 15 , 2935 (2024)..

Zhang, Y. L. et al. Large depth-of-field ultra-compact microscope by progressive optimization and deep learning. Nat. Commun. 14 , 4118 (2023)..

Atalay Appak, I. A. et al. Learning flat optics for extended depth of field microscopy imaging. Nanophotonics 12 , 3623–3632 (2023)..

Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutionalnetworks for biomedical image segmenta tion. In Proc. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention 234–241 (Springer, 2015).

Imamoglu, N. et al. Hyperspectral image dataset for benchmarking on salient object detection. In Proc. 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) . 1–3 (IEEE, 2018).

Kingma, D. P.&Ba, J. Adam: a method for stochastic optimization. In 3rd International Conference on Learning Representations, Conference Track Proceedings (ICLR, 2015).

Tse, J. R. & Engler, A. J. Preparation of hydrogel substrates with tunable mechanical properties. Curr. Protoc. Cell Biol. 47 , 10.16.1–10.16.16 (2010)..

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

On the precision models of fringe projection profilometry: unification, simplification and connection

Generating vectorial optical fields via surface-wave-excited complex-amplitude metasurfaces

Nanosecond-latency all-optical fiber sensing with in-sensor computing

Two-photon states meet polarization-gradient metasurfaces for nanometric, low-dose lateral-displacement metrology

Reversible optical data storage and encryption enabled by phase-change and hydrogel integration

Related Author

No data

Related Institution

No data

⁰