Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography

Shiyi Cheng; Shuaibin Chang; Yunzhe Li; Anna Novoseltseva; Sunni Lin; Yicun Wu; Jiahui Zhu; Ann C. McKee; Douglas L. Rosene; Hui Wang; Irving J. Bigio; David A. Boas; Lei Tian

doi:10.1038/s41377-024-01658-0

Go to Nature Website

Your Location：

Home >

Browse articles >

Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography

Original Articles

- Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography
- Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography
- LSA 2025年14卷第2期页码：490-508
- Affiliations：
  
  1.Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
  2.Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
  3.Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
  4.Department of Computer Science, Boston University, Boston, MA 02215, USA
  5.Boston University Alzheimer's Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
  6.Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
  7.VA Boston Healthcare System, U.S. Department of Veteran Affairs, Boston, MA 02130, USA
  8.Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA 02118, USA
  9.Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA
  10.Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129, USA
  11.Neurophotonics Center, Boston University, Boston, MA 02215, USA
- Author bio：
  
  Lei Tian (leitian@bu.edu)
- Funds：
- DOI：10.1038/s41377-024-01658-0
  中图分类号：
- 收稿日期：2024-03-04，
  
  修回日期：2024-09-29，
  
  录用日期：2024-10-11，
  
  网络出版日期：2025-01-20，
  
  纸质出版日期：2025-02-28
- Accepted：
Scan QR Code
Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography[J]. LSA, 2025,14(2):490-508.

Cheng, S. Y. et al. Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography. Light: Science & Applications, 14, 490-508 (2025).
Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography[J]. LSA, 2025,14(2):490-508. DOI： 10.1038/s41377-024-01658-0.

Cheng, S. Y. et al. Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography. Light: Science & Applications, 14, 490-508 (2025). DOI： 10.1038/s41377-024-01658-0.

摘要

Abstract

A major challenge in neuroscience is visualizing the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features but suffers from staining variability

tissue damage

and distortion

which impedes accurate 3D reconstructions. The emerging label-free serial sectioning optical coherence tomography (S-OCT) technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features. Here

we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining (DS) model. This enhanced imaging modality integrates high-throughput 3D imaging

low sample variability and high interpretability

making it suitable for 3D histology studies. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples

achieving consistent staining quality and enhancing contrast across cortical layer boundaries. Additionally

we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks

allowing for visualization of meso-scale vessel networks in the white matter. We believe that our technique has the potential for high-throughput

multiscale imaging of brain tissues and may facilitate studies of brain structures.

关键词

Keywords

references

Herculano-Houzel, S. The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost. Proc. Natl Acad. Sci. USA 109 , 10661–10668 (2012)..

Pistorio, A. L., Hendry, S. H. & Wang, X. Q. A modified technique for high-resolution staining of myelin. J. Neurosci. Methods 153 , 135–146 (2006)..

Kuninaka, N. et al. Simplification of the modified Gallyas method. Neuropathology 35 , 10–15 (2015)..

Amunts, K. et al. BigBrain: an ultrahigh-resolution 3D human brain model. Science 340 , 1472–1475 (2013)..

Yushkevich, P. A. et al. 3D mouse brain reconstruction from histology using a coarse-to-fine approach. Proc. 3rd Biomedical Image Registration (Utrecht: Springer, 2006).

Osechinskiy, S. & Kruggel, F. Slice-to-volume nonrigid registration of histological sections to MR images of the human brain. Anat. Res. Int. 2011 , 287860 (2011)..

Min, E. et al. Serial optical coherence microscopy for label-free volumetric histopathology. Sci. Rep. 10 , 6711 (2020)..

Cao, R. et al. Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy. Nat. Biomed. Eng. 7 , 124–134 (2023)..

Zhang, Y. et al. Three-dimensional label-free histological imaging of whole organs by microtomy-assisted autofluorescence tomography. iScience 25 , 103721 (2022)..

Sun, Y. et al. Real-time three-dimensional histology-like imaging by label-free nonlinear optical microscopy. Quant. Imaging Med. Surg. 10 , 2177190–2172190 (2020)..

Wang, H. et al. Characterizing the optical properties of human brain tissue with high numerical aperture optical coherence tomography. Biomed. Opt. Express 8 , 5617–5636 (2017)..

Yang, J. R. et al. Improving the characterization of ex vivo human brain optical properties using high numerical aperture optical coherence tomography by spatially constraining the confocal parameters. Neurophotonics 7 , 045005 (2020)..

Magnain, C. et al. Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann's area 32 and area 21. Brain Struct. Funct. 224 , 351–362 (2019)..

Yang, J. R. et al. Volumetric characterization of microvasculature in ex vivo human brain samples by serial sectioning optical coherence tomography. IEEE Trans. Biomed. Eng. 69 , 3645–3656 (2022)..

Magnain, C. et al. Blockface histology with optical coherence tomography: a comparison with Nissl staining. NeuroImage 84 , 524–533 (2014)..

Magnain, C. et al. Optical coherence tomography visualizes neurons in human entorhinal cortex. Neurophotonics 2 , 015004 (2015)..

Wang, H. et al. as-PSOCT: volumetric microscopic imaging of human brain architecture and connectivity. NeuroImage 165 , 56–68 (2018)..

Liu, C. J. et al. Visualizing and mapping the cerebellum with serial optical coherence scanner. Neurophotonics 4 , 011006 (2016)..

Wang, H., Zhu, J. F. & Akkin, T. Serial optical coherence scanner for large-scale brain imaging at microscopic resolution. NeuroImage 84 , 1007–1017 (2014)..

Odgaard, A. et al. A direct method for fast three-dimensional serial reconstruction. J. Microsc. 159 , 335–342 (1990)..

Chang, S. B. et al. Scalable mapping of myelin and neuron density in the human brain with micrometer resolution. Sci. Rep. 12 , 363 (2022)..

Bai, B. J. et al. Deep learning-enabled virtual histological staining of biological samples. Light Sci. Appl. 12 , 57 (2023)..

Salido, J. et al. Comparison of deep learning models for digital H & E staining from unpaired label-free multispectral microscopy images. Computer Methods Prog. Biomed. 235 , 107528 (2023)..

Rivenson, Y. et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl. 8 , 23 (2019)..

Rivenson, Y. et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng. 3 , 466–477 (2019)..

De Haan, K. et al. Deep learning-based transformation of H & E stained tissues into special stains. Nat. Commun. 12 , 4884 (2021)..

Winetraub, Y. et al. Noninvasive virtual biopsy using micro-registered optical coherencetomography (OCT) in human subjects. Sci. Adv. 10 , eadi5794 (2024)..

Li, X. Y. et al. Unsupervised content-preserving transformation for optical microscopy. Light Sci. Appl. 10 , 44 (2021)..

Ozyoruk, K. B. et al. A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded. Nat. Biomed. Eng. 6 , 1407–1419 (2022)..

Combalia, M. et al. Digitally stained confocal microscopy through deep learning. In Proc. 2nd International Conference on Medical Imaging with Deep Learning . (PMLR 2019).

Kang, L. et al. Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining. Photoacoustics 25 , 100308 (2022)..

Zhu, J. Y. et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proc. IEEE International Conference on Computer Vision . (IEEE, 2017).

Park, T. et al. Contrastive learning for unpaired image-to-image translation. In Proceedings of the 16th European Conference on Computer Vision . (Springer, 2020).

Kong, L. K. et al. Breaking the dilemma of medical image-to-image translation. In Proc. 35th International Conference on Neural Information Processing Systems . (ACM, 2021).

Qin, C. et al. Unsupervised deformable registration for multi-modal images via disentangled representations. In Proc. 26th International Conference on Information Processing in Medical Imaging . (Springer, 2019).

Arar, M. et al. Unsupervised multi-modal image registration via geometry preserving image-to-image translation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition . (IEEE, 2020).

Ganin, Y. et al. Domain-adversarial training of neural networks. in Domain Adaptation in Computer Vision Applications (ed Csurka, G. ) (Cham: Springer, 2017).

Chang, S. B. et al. Multi-scale label-free human brain imaging with integrated serial sectioning polarization sensitive optical coherence tomography and two-photon microscopy. Adv. Sci. 10 , 2303381 (2023)..

Varadarajan, D. et al. A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images. NeuroImage 257 , 119304 (2022)..

Palomero-Gallagher, N. & Zilles, K. Cortical layers: cyto-, myelo-, receptor- and synaptic architecture in human cortical areas. NeuroImage 197 , 716–741 (2019)..

Turner, R. Myelin and modeling: bootstrapping cortical microcircuits. Front. Neural Circuits 13 , 34 (2019)..

Lin, D. J., Hermann, K. L. & Schmahmann, J. D. The diagnosis and natural history of multiple system atrophy, cerebellar type. Cerebellum 15 , 663–679 (2016)..

Halliday, G. M. Re-evaluating the glio-centric view of multiple system atrophy by highlighting the neuronal involvement. Brain 138 , 2116–2119 (2015)..

DeFelipe, J., Alonso-Nanclares, L. & Arellano, J. I. Microstructure of the neocortex: comparative aspects. J. Neurocytol. 31 , 299–316 (2002)..

Wagstyl, K. et al. BigBrain 3D atlas of cortical layers: cortical and laminar thickness gradients diverge in sensory and motor cortices. PLoS Biol. 18 , e3000678 (2020)..

Pang, C. C. C. et al. Ammon's horn 2 (CA2) of the hippocampus: a long-known region with a new potential role in neurodegeneration. Neuroscientist 25 , 167–180 (2019)..

Perge, J. A. et al. Why do axons differ in caliber? J. Neurosci. 32 , 626–638 (2012)..

Molini, A. B. et al. Speckle2Void: deep self-supervised SAR despeckling with blind-spot convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 60 , 5204017 (2022)..

Liu, R. R. et al. Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification. Light Sci. Appl. 8 , 102 (2019)..

Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to Ima geJ: 25 years of image analysis. Nat. Methods 9 , 671–675 (2012)..

Fan, J., Dawson, T. M.&Dawson, V. L. Cell death mechanisms of neurodegeneration. in Neurodegenerative Diseases Pathology, Mechanisms, and Potential Therapeutic Targets (eds Beart, P. et al. ) (Cham: Springer, 2017).

Reinhard, E. et al. Color transfer between images. IEEE Computer Graph. Appl. 21 , 34–41 (2001)..

Van Der Walt, S. et al. Scikit-image: image processing in Python. PeerJ 2 , e453 (2014)..

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