Neural network-based processing and reconstruction of compromised biophotonic image data

Michael John Fanous; Paloma Casteleiro Costa; Çağatay Işıl; Luzhe Huang; Aydogan Ozcan

doi:10.1038/s41377-024-01544-9

Your Location：

Home >

Browse articles >

Neural network-based processing and reconstruction of compromised biophotonic image data

Reviews

- Neural network-based processing and reconstruction of compromised biophotonic image data
- Neural network-based processing and reconstruction of compromised biophotonic image data
- LSA 2024年13卷第10期页码：2101-2113
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
  2.Bioengineering Department, University of California, Los Angeles, CA, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
  4.Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-024-01544-9
  中图分类号：
- 收稿日期：2024-03-22，
  
  修回日期：2024-07-16，
  
  录用日期：2024-07-18，
  
  网络出版日期：2024-09-04，
  
  纸质出版日期：2024-10-31
- Accepted：
Scan QR Code
Neural network-based processing and reconstruction of compromised biophotonic image data[J]. LSA, 2024,13(10):2101-2113.

Fanous, M. J. et al. Neural network-based processing and reconstruction of compromised biophotonic image data. Light: Science & Applications, 13, 2101-2113 (2024).
Neural network-based processing and reconstruction of compromised biophotonic image data[J]. LSA, 2024,13(10):2101-2113. DOI： 10.1038/s41377-024-01544-9.

Fanous, M. J. et al. Neural network-based processing and reconstruction of compromised biophotonic image data. Light: Science & Applications, 13, 2101-2113 (2024). DOI： 10.1038/s41377-024-01544-9.

摘要

Abstract

In recent years

the integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of e.g.

cost

speed

and form-factor

followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal

superior or alternative data. This strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic imaging. One of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed

critical for capturing fine dynamic biological processes. Additionally

this approach offers the prospect of simplifying hardware requirements and complexities

thereby making advanced imaging standards more accessible in terms of cost and/or size. This article provides an in-depth review of the diverse measurement aspects that researchers intentionally impair in their biophotonic setups

including the point spread function (PSF)

signal-to-noise ratio (SNR)

sampling density

and pixel resolution. By deliberately compromising these metrics

researchers aim to not only recuperate them through the application of deep learning networks

but also bolster in return other crucial parameters

such as the field of view (FOV)

depth of field (DOF)

and space-bandwidth product (SBP). Throughout this article

we discuss various biophotonic methods that have successfully employed this strategic approach. These techniques span a wide range of applications and showcase the versatility and effectiveness of deep learning in the context of compromised biophotonic data. Finally

by offering our perspectives on the exciting future possibilities of this rapidly evolving concept

we hope to motivate our readers from various disciplines to explore novel ways of balancing hardware compromises with compensation via artificial intelligence (AI).

关键词

Keywords

references

Prasad, P. N. Introduction to Biophotonics (Hoboken: John Wiley & Sons, 2003).

Marcu, L. et al. Biophotonics: the big picture. J. Biomed. Opt. 23 , 021103 (2017)..

Tian, L. et al. Deep learning in biomedical optics. Lasers Surg. Med. 53 , 748–775 (2021)..

Pradhan, P. et al. Deep learning a boon for biophotonics? J. Biophotonics 13 , e201960186 (2020)..

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

Diaspro, A. et al. in Handbook of Biological Confocal Microscopy 3rd edn (ed Pawley, J. B.) (New York: Springer, 2006).

Demchenko, A. P. Photobleaching of organic fluorophores: quantitative characterization, mechanisms, protection. Methods Appl. Fluoresc. 8 , 022001 (2020)..

Luo, Y. L. et al. Single-shot autofocusing of microscopy images using deep learning. ACS Photonics 8 , 625–638 (2021)..

Yang, X. L. et al. Deep-learning-based virtual refocusing of images using an engineered point-spread function. ACS Photonics 8 , 2174–2182 (2021)..

Fanous, M. J. & Popescu, G. GANscan: continuous scanning microscopy using deep learning deblurring. Light Sci. Appl. 11 , 265, https://doi.org/10.1038/s41377-022-00952-z (2022)..

Chen, H. L. et al. eFIN: enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging. IEEE J. Sel. Top. Quantum Electron. 29 , 6800810 (2023)..

Huang, L. Z. et al. Recurrent neural network-based volumetric fluorescence microscopy. Light Sci. Appl. 10 , 62 (2021)..

Huang, L. Z. et al. Few-shot transfer learning for holographic image reconstruction using a recurrent neural network. APL Photonics 7 , 070801 (2022)..

Zhang, Y. J. et a l. Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data. Light Sci. Appl. 10 , 155 (2021)..

Cheng, Y. F. et al. Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy. Opt. express 27 , 644–656 (2019)..

Rivenson, Y. et al. Deep learning enhanced mobile-phone microscopy. ACS Photonics 5 , 2354–2364 (2018)..

Yao, X. et al. Increasing a microscope's effective field of view via overlapped imaging and machine learning. Opt. Express 30 , 1745–1761, https://doi.org/10.1364/OE.445001 (2022)..

Jin, L. H. et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed. Nat. Commun. 11 , 1934, https://doi.org/10.1038/s41467-020-15784-x (2020)..

Manifold, B. et al. Denoising of stimulated Raman scattering microscopy images via deep learning. Biomed. Opt. Express 10 , 3860–3874 (2019)..

Pinkard, H. et al. Deep learning for single-shot autofocus microscopy. Optica 6 , 794–797 (2019)..

Ebrahimi, V. et al. Deep learning enables fast, gentle STED microscopy. Commun. Biol. 6 , 674 (2023)..

Botcherby, E. J. et al. An optical technique for remote focusing in microscopy. Opt. Commun. 281 , 880–887 (2008)..

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

Mohanan, S. & Corbett, A. D. Understanding the limits of remote focusing. Opt. Express 31 , 16281–16294 (2023)..

Rossmann, K. Point spread-function, line spread-function, and modulation transfer function: tools for the study of imaging systems. Radiology 93 , 257–272 (1969)..

Jouchet, P., Roy, A. R. & Moerner, W. E. Combining deep learning approaches and point spread function engineering for simultaneous 3D position and 3D orientation measurements of fluorescent single molecules. Opt. Commun. 542 , 129589 (2023)..

Astratov, V. N. et al. Roadmap on label‐free super‐resolution imaging. Laser Photonics Rev. 17 , 2200029 (2023)..

Nehme, E. et al. DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning. Nat. Methods 17 , 734–740 (2020)..

Vaquero, D. et al. Generalized autofocus. In Proc. 2011 IEEE Workshop on applications of computer vision (WACV) . 511–518 (IEEE: Kona, HI, USA, 2011).

Pavani, S. R. P. et al. Three-dimensional, single-molecule fluorescence imaging beyond the diffraction limit by using a double-helix point spread function. Proc. Natl Acad. Sci. U. Stateds Am. 106 , 2995–2999 (2009)..

Wang, H. D. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16 , 103–110 (2019)..

Wu, Y. C. et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica 5 , 704–710 (2018)..

Huang, L. Z. et al. Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks. ACS Photonics 8 , 1763–1774 (2021)..

Huang, L. Z. et al. Self-supervised learning of hologram reconstruction using physics consistency. Nat. Mach. Intell. 5 , 895–907 (2023)..

Pirone, D. et al. Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning. Lab Chip 22 , 793–804 (2022)..

Park, J. et al. Revealing 3D cancer tissue structures using holotomography and virtual hematoxylin and eosin staining via deep learning. Preprint at https://www.biorxiv.org/content/10.1101/2023.12.04.569853v2 https://www.biorxiv.org/content/10.1101/2023.12.04.569853v2 (2023).

Barbastathis, G., Ozcan, A. & Situ, G. H. On the use of deep learning for computational imaging. Optica 6 , 921–943 (2019)..

Situ, G. H. Deep holography. Light Adv. Manuf. 3 , 13 (2022)..

Kakkava, E. et al. Imaging through multimode fibers using deep learning: the effects of intensity versus holographic recording of the speckle pattern. Opt. Fiber Technol. 52 , 101985 (2019)..

Park, J. et al. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat. Methods 20 , 1645–1660 (2023)..

Chen, H. L. et al. Fourier Imager Network (FIN): a deep neural network for hologram reconstruction with superior external generalization. Light Sci. Appl. 11 , 254 (2022)..

Goodfellow, I. et al. Generative adversarial networks. Commun. ACM 63 , 139–144 (2020)..

Lepage, G., Bogaerts, J. & Meynants, G. Time-delay-integration architectures in CMOS image sensors. IEEE Trans. Electron Devices 56 , 2524–2533 (2009)..

Ren, Z. B., Xu, Z. M. & Lam, E. Y. Learning-based nonparametric autofocusing for digital holography. Optica 5 , 337–344 (2018)..

Konda, P. C. et al. Fourier ptychography: current applications and future promises. Opt. express 28 , 9603–9630 (2020)..

Zheng, G. A., Horstmeyer, R. & Yang, C. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics 7 , 739–745 (2013)..

Tian, L. et al. Multiplexed coded illumination for Fourier Ptychography with an LED array microscope. Biomed. Opt. Express 5 , 2376–2389 (2014)..

Nguyen, T. et al. Deep learning approach for Fourier ptychography microscopy. Opt. Express 26 , 26470–26484 (2018)..

Grossberg, S. Recurrent neural networks. Scholarpedia 8 , 1888 (2013)..

Podoleanu, A. G. Optical coherence tomography. Br. J. Radiol. 78 , 976–988 (2005)..

Kim, M. K. Principles and techniques of digital holographic microscopy. SPIE Rev. 1 , 018005 (2010)..

Stelzer Contrast, resolution, pixelation, dynamic range and signal‐to‐noise ratio: fundamental limits to resolution in fluorescence light microscopy. J. Microsc. 189 , 15–24 (1998)..

Rittweger, E. et al. STED microscopy reveals crystal colour centres with nanometric resolution. Nat. Photonics 3 , 144–147 (2009)..

Tipping, W. J. et al. Stimulated Raman scattering microscopy: an emerging tool for drug discovery. Chem. Soc. Rev. 45 , 2075–2089 (2016)..

Saxena, M., Eluru, G. & Gorthi, S. S. Structured illumination microscopy. Adv. Opt. Photonics 7 , 241–275 (2015)..

Wu, Y. C. et al. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning. Nat. Methods 16 , 1323–1331 (2019)..

Repetti, A., Pereyra, M. & Wiaux, Y. Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization. SIAM J. Imaging Sci. 12 , 87–118 (2019)..

Zhou, Q. P. et al. Bayesian inference and uncertainty quantification for medical image reconstruction with Poisson data. SIAM J. Imaging Sci. 13 , 29–52 (2020)..

Xue, Y. J. et al. Reliable deep-learning-based phase imaging with unce rtainty quantification. Optica 6 , 618–629 (2019)..

Hoffmann, L., Fortmeier, I. & Elster, C. Uncertainty quantification by ensemble learning for computational optical form measurements. Mach. Learn. Sci. Technol. 2 , 035030 (2021)..

Huang, L. Z. et al. Cycle-consistency-based uncertainty quantification of neural networks in inverse imaging problems. Intell. Comput. 2 , 0071 (2023)..

Chen, J. T. et al. A transfer learning based super-resolution microscopy for biopsy slice images: the joint methods perspective. IEEE/ACM Trans. Comput. Biol. Bioinform. 18 , 103–113 (2021)..

Christensen, C. N. et al. ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning. Biomed. Opt. Express 12 , 2720–2733 (2021)..

Shi, X. J. et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In Proc. 28 th Internal Conference on Neural Information Processing Systems (Montreal, Canada: MIT Press, 2015)..

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据