Abstract

Obtaining fine structures of neurons is necessary for understanding brain function. Simple and effective methods for large-scale 3D imaging at optical resolution are still lacking. Here, we proposed a deep-learning-based fluorescence micro-optical sectioning tomography (DL-fMOST) method for high-throughput, high-resolution whole-brain imaging. We utilized a wide-field microscope for imaging, a U-net convolutional neural network for real-time optical sectioning, and histological sectioning for exceeding the imaging depth limit. A 3D dataset of a mouse brain with a voxel size of 0.32 × 0.32 × 2 µm was acquired in 1.5 days. We demonstrated the robustness of DL-fMOST for mouse brains with labeling of different types of neurons.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2020 (1)

K. de Haan, Y. Rivenson, Y. Wu, and A. Ozcan, “Deep-learning-based image reconstruction and enhancement in optical microscopy,” Proc. IEEE 108(1), 30–50 (2020).
[Crossref]

2019 (6)

C. Belthangady and L. A. Royer, “Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction,” Nat. Methods 16(12), 1215–1225 (2019).
[Crossref]

Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. de Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25 (2019).
[Crossref]

Y. Rivenson, T. Liu, Z. Wei, Y. Zhang, K. de Haan, and A. Ozcan, “PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning,” Light: Sci. Appl. 8(1), 23 (2019).
[Crossref]

B. Manifold, E. Thomas, A. T. Francis, A. H. Hill, and D. Fu, “Denoising of stimulated Raman scattering microscopy images via deep learning,” Biomed. Opt. Express 10(8), 3860–3874 (2019).
[Crossref]

J. Winnubst, E. Bas, T. A. Ferreira, Z. Wu, M. N. Economo, P. Edson, B. J. Arthur, C. Bruns, K. Rokicki, D. Schauder, D. J. Olbris, S. D. Murphy, D. G. Ackerman, C. Arshadi, P. Baldwin, R. Blake, A. Elsayed, M. Hasan, D. Ramirez, B. Dos Santos, M. Weldon, A. Zafar, J. T. Dudman, C. R. Gerfen, A. W. Hantman, W. Korff, S. M. Sternson, N. Spruston, K. Svoboda, and J. Chandrashekar, “Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain,” Cell 179(1), 268–281.e13 (2019).
[Crossref]

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydın, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16(1), 103–110 (2019).
[Crossref]

2018 (5)

C. Ounkomol, S. Seshamani, M. M. Maleckar, F. Collman, and G. R. Johnson, “Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy,” Nat. Methods 15(11), 917–920 (2018).
[Crossref]

M. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, S. Culley, M. Rocha-Martins, F. Segovia-Miranda, C. Norden, R. Henriques, M. Zerial, M. Solimena, J. Rink, P. Tomancak, L. Royer, F. Jug, and E. W. Myers, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” Nat. Methods 15(12), 1090–1097 (2018).
[Crossref]

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref]

X. Zhang, Y. Chen, K. Ning, C. Zhou, Y. Han, H. Gong, and J. Yuan, “Deep learning optical-sectioning method,” Opt. Express 26(23), 30762–30772 (2018).
[Crossref]

X. Yang, Q. Zhang, F. Huang, K. Bai, Y. Guo, Y. Zhang, N. Li, Y. Cui, P. Sun, S. Zeng, and X. Lv, “High-throughput light sheet tomography platform for automated fast imaging of whole mouse brain,” J. Biophotonics 11(9), e201800047 (2018).
[Crossref]

2017 (2)

K. Seiriki, A. Kasai, T. Hashimoto, W. Schulze, M. Niu, S. Yamaguchi, T. Nakazawa, K.-i. Inoue, S. Uezono, M. Takada, Y. Naka, H. Igarashi, M. Tanuma, J. A. Waschek, Y. Ago, K. F. Tanaka, A. Hayata-Takano, K. Nagayasu, N. Shintani, R. Hashimoto, Y. Kunii, M. Hino, J. Matsumoto, H. Yabe, T. Nagai, K. Fujita, T. Matsuda, K. Takuma, A. Baba, and H. Hashimoto, “High-speed and scalable whole-brain imaging in rodents and primates,” Neuron 94(6), 1085–1100.e6 (2017).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

2016 (4)

M. N. Economo, N. G. Clack, L. D. Lavis, C. R. Gerfen, K. Svoboda, E. W. Myers, and J. Chandrashekar, “A platform for brain-wide imaging and reconstruction of individual neurons,” eLife 5, e10566 (2016).
[Crossref]

H. Gong, D. Xu, J. Yuan, X. Li, C. Guo, J. Peng, Y. Li, L. A. Schwarz, A. Li, B. Hu, B. Xiong, Q. Sun, Y. Zhang, J. Liu, Q. Zhong, T. Xu, S. Zeng, and Q. Luo, “High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level,” Nat. Commun. 7(1), 12142 (2016).
[Crossref]

N. Renier, E. L. Adams, C. Kirst, Z. Wu, R. Azevedo, J. Kohl, A. E. Autry, L. Kadiri, K. Umadevi Venkataraju, Y. Zhou, V. X. Wang, C. Y. Tang, O. Olsen, C. Dulac, P. Osten, and M. Tessier-Lavigne, “Mapping of brain activity by automated volume analysis of immediate early genes,” Cell 165(7), 1789–1802 (2016).
[Crossref]

T. Quan, H. Zhou, J. Li, S. Li, A. Li, Y. Li, X. Lv, Q. Luo, H. Gong, and S. Zeng, “NeuroGPS-Tree: automatic reconstruction of large-scale neuronal populations with dense neurites,” Nat. Methods 13(1), 51–54 (2016).
[Crossref]

2015 (2)

J. Yuan, H. Gong, A. Li, X. Li, S. Chen, S. Zeng, and Q. Luo, “Visible rodent brain-wide networks at single-neuron resolution,” Front. Neuroanat. 9, 70 (2015).
[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

2014 (6)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

E. A. Susaki, K. Tainaka, D. Perrin, F. Kishino, T. Tawara, T. M. Watanabe, C. Yokoyama, H. Onoe, M. Eguchi, S. Yamaguchi, T. Abe, H. Kiyonari, Y. Shimizu, A. Miyawaki, H. Yokota, and H. R. Ueda, “Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis,” Cell 157(3), 726–739 (2014).
[Crossref]

B. Zingg, H. Hintiryan, L. Gou, M. Y. Song, M. Bay, M. S. Bienkowski, N. N. Foster, S. Yamashita, I. Bowman, A. W. Toga, and H.-W. Dong, “Neural networks of the mouse neocortex,” Cell 156(5), 1096–1111 (2014).
[Crossref]

B. J. Hunnicutt, B. R. Long, D. Kusefoglu, K. J. Gertz, H. Zhong, and T. Mao, “A comprehensive thalamocortical projection map at the mesoscopic level,” Nat. Neurosci. 17(9), 1276–1285 (2014).
[Crossref]

S. W. Oh, J. A. Harris, L. Ng, B. Winslow, N. Cain, S. Mihalas, Q. Wang, C. Lau, L. Kuan, A. M. Henry, M. T. Mortrud, B. Ouellette, T. N. Nguyen, S. A. Sorensen, C. R. Slaughterbeck, W. Wakeman, Y. Li, D. Feng, A. Ho, E. Nicholas, K. E. Hirokawa, P. Bohn, K. M. Joines, H. Peng, M. J. Hawrylycz, J. W. Phillips, J. G. Hohmann, P. Wohnoutka, C. R. Gerfen, C. Koch, A. Bernard, C. Dang, A. R. Jones, and H. Zeng, “A mesoscale connectome of the mouse brain,” Nature 508(7495), 207–214 (2014).
[Crossref]

Y. Sun, A. Q. Nguyen, J. P. Nguyen, L. Le, D. Saur, J. Choi, E. M. Callaway, and X. Xu, “Cell-type-specific circuit connectivity of hippocampal CA1 revealed through Cre-dependent rabies tracing,” Cell Rep. 7(1), 269–280 (2014).
[Crossref]

2013 (5)

D. Zhu, K. V. Larin, Q. Luo, and V. V. Tuchin, “Recent progress in tissue optical clearing,” Laser Photonics Rev. 7(5), 732–757 (2013).
[Crossref]

K. Chung and K. Deisseroth, “CLARITY for mapping the nervous system,” Nat. Methods 10(6), 508–513 (2013).
[Crossref]

T. Zheng, Z. Yang, A. Li, X. Lv, Z. Zhou, X. Wang, X. Qi, S. Li, Q. Luo, H. Gong, and S. Zeng, “Visualization of brain circuits using two-photon fluorescence micro-optical sectioning tomography,” Opt. Express 21(8), 9839–9850 (2013).
[Crossref]

H. Gong, S. Zeng, C. Yan, X. Lv, Z. Yang, T. Xu, Z. Feng, W. Ding, X. Qi, A. Li, J. Wu, and Q. Luo, “Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution,” NeuroImage 74, 87–98 (2013).
[Crossref]

T. Quan, T. Zheng, Z. Yang, W. Ding, S. Li, J. Li, H. Zhou, Q. Luo, H. Gong, and S. Zeng, “NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model,” Sci. Rep. 3(1), 1414 (2013).
[Crossref]

2012 (1)

T. Ragan, L. R. Kadiri, K. U. Venkataraju, K. Bahlmann, J. Sutin, J. Taranda, I. Arganda-Carreras, Y. Kim, H. S. Seung, and P. Osten, “Serial two-photon tomography for automated ex vivo mouse brain imaging,” Nat. Methods 9(3), 255–258 (2012).
[Crossref]

2011 (1)

J. W. Lichtman and W. Denk, “The big and the small: Challenges of imaging the brain’s circuits,” Science 334(6056), 618–623 (2011).
[Crossref]

2010 (1)

A. Li, H. Gong, B. Zhang, Q. Wang, C. Yan, J. Wu, Q. Liu, S. Zeng, and Q. Luo, “Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain,” Science 330(6009), 1404–1408 (2010).
[Crossref]

2007 (1)

H.-U. Dodt, U. Leischner, A. Schierloh, N. Jährling, C. P. Mauch, K. Deininger, J. M. Deussing, M. Eder, W. Zieglgänsberger, and K. Becker, “Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain,” Nat. Methods 4(4), 331–336 (2007).
[Crossref]

2004 (2)

L. H. Schaefer, D. Schuster, and J. Schaffer, “Structured illumination microscopy: artefact analysis and reduction utilizing a parameter optimization approach,” J. Microsc. 216(2), 165–174 (2004).
[Crossref]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref]

2002 (1)

G. M. G. Shepherd, M. Raastad, and P. Andersen, “General and variable features of varicosity spacing along unmyelinated axons in the hippocampus and cerebellum,” Proc. Natl. Acad. Sci. U. S. A. 99(9), 6340–6345 (2002).
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2000 (1)

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Figures (11)

Fig. 1.
Fig. 1. Principle of DL-fMOST. (a) System configuration and imaging strategy. TL, tube lens; EX, excitation filter; DM, dichroic mirror; PZT, piezoelectric translational stage; Obj, objective; EM, emission filter. (b) Optical sectioning enabled by the trained convolutional neural network.
Fig. 2.
Fig. 2. Typical training data for DL-fMOST. The WF images and the corresponding SIM images are taken from the same dataset at the positions of Bregma 0.86 mm (a), −0.22 mm (b), −1.22 mm (c), −2.54 mm (d), and −3.80 mm (e). Scale bar: 100 µm.
Fig. 3.
Fig. 3. Network architecture. The numbers on the top of the block indicate the output channels of each convolutional layer, and the numbers on the bottom of the block represent the size of the feature map. The operations are represented by arrows of different colors.
Fig. 4.
Fig. 4. DL-fMOST imaging of a Thy1-GFP M line mouse brain. (a)–(c) 200-µm-thick MIPs of different coronal sections (2 mm interval) reconstructed by SIM algorithm and CNN prediction. Arrows at the top right corner indicate the locations of the coronal sections. The colored lines in (c) mark two representative brain regions of remarkably different neuron distributions. (d) Enlarged views of the white rectangles in (c). The WF image is shown for comparison. (e) The intensity profiles on color lines of the corresponding images. Scale bar, 2 mm (100 µm for the inset).
Fig. 5.
Fig. 5. Quantitative performance of DL-fMOST across the whole-brain dataset. RMSE, root mean squared error; SSIM, structural similarity index.
Fig. 6.
Fig. 6. 3D imaging capability of DL-fMOST. (a) 3D reconstruction of a data block extracted from the test set 1. (b,c) MIPs of the SIM volume and CNN volume in three coordinate directions. The intensity profiles are plotted according to the colored dashed lines on the xz (d) and yz (e) planes, where the red arrows indicate the local peaks of the neural fibers. Scale bar: 100 µm.
Fig. 7.
Fig. 7. 3D reconstruction of a GFP-labeled image stack and MIPs in the XY direction and YZ direction using (a) SIM algorithm and (b) CNN. (c) Merged image of (a) and (b), the mapped areas are shown in yellow. The white arrows indicate that the signal in the corner of the FOV will be enhanced by CNN. The insets show the magnifications of the areas marked with white solid lines. The image block is selected from the same whole-brain dataset as in Fig. 4. Scale bar, 100 µm (20 µm in the inset).
Fig. 8.
Fig. 8. SNR improvement and artifacts elimination in DL-fMOST. (a) SIM-reconstructed image and (b) CNN-reconstructed image. Both images had been given the same contrast stretch to improve the display. Scale bar, 100 µm (20 µm in the inset)
Fig. 9.
Fig. 9. Imaging mouse brains of different labeling targets. (a)–(d) Typical coronal MIPs of four mouse brains (test set 2-5) with different projection patterns reconstructed by the same Thy1-GFP-M trained CNN. The MIP thicknesses are 200 µm. Scale bar, 2 mm (100 µm for the inset).
Fig. 10.
Fig. 10. Quantitative cell counting comparison. Automatically locating soma centers in the (a) SIM data and (b) CNN data using NeuroGPS algorithm (red dots). Blue arrows indicate cell bodies that were not correctly identified in the SIM block, but accurately identified in the CNN block. The green arrow marks the opposite of the blue arrows. Erroneous identifications are indicated by orange arrows. All missed somas in both data blocks are marked by purple arrows. (c) Precision and (d) recall rates for both SIM and CNN image stacks.
Fig. 11.
Fig. 11. Neuron morphological reconstruction comparison. (a) 500 µm MIPs of the image stack along the Z direction. (b)-(c) 200 µm MIPs derived from the SIM and CNN reconstruction. (d) Semi-automated tracing results based on (b, c). Scale bar, 100 µm.

Equations (6)

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{ I W F  =  1 3 ( I 1  +  I 2  +  I 3 ) I S I M  =  [ ( I 1 I 2 ) 2 + ( I 2 I 3 ) 2 + ( I 3 I 1 ) 2 ] 1 / 2
L M A E = 1 K H W k = 1 K i = 1 H j = 1 W | ψ θ k ( i , j ) P ( i , j ) |
R M S E = [ 1 H W i = 1 H j = 1 W ( M ( i , j ) N ( i , j ) ) 2 ] 1 / 2
S S I M  =  ( 2 μ M μ N  +  C 1 ) ( 2 σ M N  +  C 2 ) ( μ M 2 + μ N 2 + C 1 ) ( σ M 2 + σ N 2 + C 2 )
S N R  =  | p b σ b |
S B R = p b .

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