PhotoniX, Volume. 4, Issue 1, 34(2023)

Digital staining in optical microscopy using deep learning - a review

Lucas Kreiss1,2、*, Shaowei Jiang3, Xiang Li4, Shiqi Xu1, Kevin C. Zhou1,5, Kyung Chul Lee1,6, Alexander Mühlberg2, Kanghyun Kim1, Amey Chaware1, Michael Ando7, Laura Barisoni8, Seung Ah Lee6, Guoan Zheng3, Kyle J. Lafata4, Oliver Friedrich2, and Roarke Horstmeyer1
Author Affiliations
  • 1Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
  • 2Institute of Medical Biotechnology, Friedrich-Alexander University (FAU), Erlangen, Germany
  • 3Department of Biomedical Engineering, University of Connecticut, Mansfield Connecticut, USA
  • 4Department of Radiation Physics, Duke University, Durham, NC 27708, USA
  • 5Department of Electrical Engineering & Computer Sciences, University of California, Berkeley CA, USA
  • 6School of Electrical & Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
  • 7Google, Inc., Mountain View, CA 94043, USA
  • 8Department of Pathology, Duke University, Durham, NC 27708, USA
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    References(166)

    [4] [4] Chen T, Chefd’Hotel C. Deep learning based automatic immune cell detection for immunohistochemistry images. In: International workshop on machine learning in medical imaging. Springer; 2014. p. 17–24.

    [5] [5] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer; 2015. p. 234–241.

    [6] [6] Liu MY, Breuel T, Kautz J. Unsupervised image-to-image translation networks. Adv Neural Inf Process Syst. 2017;30. https://proceedings.neurips.cc/paper_files/paper/2017/hash/dc6a6489640ca02b0d42dabeb8e46bb7-Abstract.html.

    [7] [7] Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. Computer Vision Foundation; 2017. p. 2223–32.

    [8] [8] Zhang Y, Tang F, Dong W, Huang H, Ma C, Lee TY, et al. Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning. arXiv preprint arXiv:2205.09542. 2022.

    [9] [9] Kingma DP, Dhariwal P. Glow: Generative flow with invertible 1x1 convolutions. Adv Neural Inf Process Syst. 2018;31.

    [11] [11] Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Adv Neural Inf Process Syst. 2020;33:6840–51.

    [12] [12] Saharia C, Chan W, Chang H, Lee C, Ho J, Salimans T, et al. Palette: Image-to-image diffusion models. In: ACM SIGGRAPH 2022 Conference Proceedings. Association for Computing Machinery; 2022. p. 1–10.

    [14] [14] Jiang H, Zhou Y, Lin Y, Chan RC, Liu J, Chen H. Deep Learning for Computational Cytology: A Survey. arXiv preprint arXiv:2202.05126. 2022.

    [17] [17] Rivenson Y, de Haan K, Wallace WD, Ozcan A. Emerging advances to transform histopathology using virtual staining. BME Front. 2020:9647163.

    [23] [23] Gupta L, Klinkhammer BM, Boor P, Merhof D, Gadermayr M. GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy. Med Image Comput Comput Assist Interv Miccai 2019, Pt I. 2019;11764:631–639. .

    [26] [26] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. Adv Neural Inf Process Syst. 2014;27. https://proceedings.neurips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html.

    [27] [27] Rana A, Yaunery G, Lowe A, Shah P. Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks. In: 2018 17th Ieee International Conference on Machine Learning and Applications (Icmla). 2018. p. 828–834. .

    [28] [28] Shaban MT, Baur C, Navab N, Albarqouni S. Staingan: Stain style transfer for digital histological images. In: 2019 Ieee 16th international symposium on biomedical imaging (Isbi 2019). IEEE; 2019. p. 953–956.

    [30] [30] Ye S, Zou J, Huang C, Xiang F, Wen Z, Wang N, et al. Rapid and label-free histological imaging of unprocessed surgical tissues via Dark-field Reflectance Ultraviolet Microscopy. iScience. 2022;105849.

    [33] [33] Bocklitz TW, Salah FS, Vogler N, Heuke S, Chernavskaia O, Schmidt C, et al. Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool. BMC Cancer. 2016;16. .

    [34] [34] de Haan K, Zhang Y, Zuckerman JE, Liu T, Sisk AE, Diaz MF, et al. Deep learning-based transformation of H &E stained tissues into special stains. Nat Commun. 2021;12(1):1–13.

    [35] [35] Opstad I. Data set: Fluorescence microscopy videos of mitochondria in H9c2 cardiomyoblasts. DataverseNO. 2023. .

    [36] [36] Hong Y, Heo YJ, Kim B, Lee D, Ahn S, Ha SY, et al. Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio. Sci Rep. 2021;11(1). .

    [41] [41] Drexler W, Fujimoto JG, et al. Optical coherence tomography: technology and applications. vol. 2. Springer; 2015.

    [43] [43] Mari JM, Aung T, Cheng CY, Strouthidis NG, Girard MJA. A Digital Staining Algorithm for Optical Coherence Tomography Images of the Optic Nerve Head. Transl Vis Sci Technol. 2017;6(1). .

    [48] [48] Cooke CL, Kong F, Chaware A, Zhou KC, Kim K, Xu R, et al. Physics-enhanced machine learning for virtual fluorescence microscopy. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. Computer Vision Foundation; 2021. p. 3803–3813.

    [49] [49] Croce AC, Bottiroli G. Autofluorescence spectroscopy and imaging: a tool for biomedical research and diagnosis. Eur J Histochem EJH. 2014;58(4):2461.

    [50] [50] Li XY, Zhang GX, Qiao H, Bao F, Deng Y, Wu JM, et al. Unsupervised content-preserving transformation for optical microscopy. Light-Sci Appl. 2021;10(1). .

    [54] [54] Schürmann S, Weber C, Fink RH, Vogel M. Myosin Rods are a Source of Second Harmonic Generation Signals in Skeletal Muscle. In: Proceedings Volume 6442, Multiphoton Microscopy in the Biomedical Sciences VII; 2007. p. 64421U. .

    [56] [56] Rosencwaig A. Photoacoustics and Photoacoustic Spectroscopy. vol. 57. Wiley; 1980. ISBN 10: 0894644505.

    [59] [59] Kang L, Li XF, Zhang Y, Wong TTW. Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining. Photoacoustics. 2022;25. .

    [60] [60] Li X, Kang L, Lo CT, Tsang VT, Wong TT. High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning. J Visualized Exp Jove. 2022;(182).

    [61] [61] Boktor M, Ecclestone B, Pekar V, Dinakaran D, Mackey JR, Fieguth P, et al. Deep-Learning-Based Virtual H&E Staining Using Total-Absorption Photoacoustic Remote Sensing (TA-PARS). In: Sci Rep. 2022;12:10296. .

    [64] [64] Stefanchik D. Endoscopic Tissue Resection Device. Google Patents; 2010. US Patent 7,780,691.

    [67] [67] Wright DK, Manos MM. Sample Preparation from Paraffin-Embedded Tissues. PCR Protocol Guide Methods Appl. 1990;19:153–9.

    [71] [71] Rivenson Y, Liu TR, Wei ZS, Zhang Y, de Haan K, Ozcan A. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light-Sci Appl. 2019;8. .

    [72] [72] Zhang YJ, de Haan K, Rivenson Y, Li JX, Delis A, Ozcan A. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue. Light-Sci Appl. 2020;9(1). .

    [73] [73] Zhang Y, de Haan K, Li J, Rivenson Y, Ozcan A. Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue. In: Conference on Lasers and Electro-Optics, Technical Digest Series (Optica Publishing Group, 2022), paper ATh2I.2.

    [74] [74] Bautista PA, Yagi Y. Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance. J Biomed Opt. 2012;17(5). .

    [76] [76] Gadermayr M, Appel V, Klinkhammer BM, Boor P, Merhof D. Which way round? A study on the performance of stain-translation for segmenting arbitrarily dyed histological images. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Lecture Notes in Computer Science. vol. 11071. Cham: Springer. 2018. p. 165–173. .

    [77] [77] Fujitani M, Mochizuki Y, Iizuka S, Simo-Serra E, Kobayashi H, Iwamoto C, et al. Re-staining pathology images by FCNN. In: 2019 16th International Conference on Machine Vision Applications (MVA). IEEE; 2019. p. 1–6.

    [81] [81] Otto F. DAPI Staining of Fixed Cells for High-Resolution Flow Cytometry of Nuclear DNA. In: Methods in Cell Biology. vol. 33. Elsevier; 1990. p. 105–10. .

    [84] [84] Cheng SY, Fu SP, Kim YM, Song WY, Li YZ, Xue YJ, et al. Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy. Sci Adv. 2021;7(3). .

    [85] [85] Yuan E, Matusiak M, Sirinukunwattana K, Varma S, Kidzinski L, West R. Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification. Front Immunol. 2021;12. .

    [86] [86] Guo SM, Yeh LH, Folkesson J, Ivanov IE, Krishnan AP, Keefe MG, et al. Revealing architectural order with quantitative label-free imaging and deep learning. Elife. 2020;9. .

    [88] [88] Burlingame EA, Margolin AA, Gray JW, Chang YH. SHIFT: speedy histopathological-to-immunofluorescent translation of whole slide images using conditional generative adversarial networks. Med Imaging 2018 Digit Pathol. 2018;10581. .

    [89] [89] Gu S, Lee RM, Benson Z, Ling C, Vitolo MI, Martin SS, et al. Label-free cell tracking enables collective motion phenotyping in epithelial monolayers. iScience. 2022;25(7):104678. .

    [90] [90] Ling C, Majurski M, Halter M, Stinson J, Plant A, Chalfoun J. Analyzing u-net robustness for single cell nucleus segmentation from phase contrast images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Computer Vision Foundation; 2020. p. 966–67.

    [91] [91] Goswami N, He YCR, Deng YH, Oh C, Sobh N, Valera E, et al. Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity. Light-Sci Appl. 2021;10(1). .

    [92] [92] Hu CF, He SH, Lee YJ, He YC, Kong EM, Li H, et al. Live-dead assay on unlabeled cells using phase imaging with computational specificity. Nat Commun. 2022;13(1). .

    [93] [93] Kolln LS, Salem O, Valli J, Hansen CG, McConnell G. Label2label: training a neural network to selectively restore cellular structures in fluorescence microscopy. J Cell Sci. 2022;135(3). .

    [96] [96] Tondeleir D, Lambrechts A, Müller M, Jonckheere V, Doll T, Vandamme D, et al. Cells lacking β-actin are genetically reprogrammed and maintain conditional migratory capacity. Mol Cell Proteomics. 2012;11(8):255–71.

    [97] [97] Anti-MAP2 antibody Data sheet [EPR19691] ab183830. Accessed Oct 2023. https://www.abcam.com/products/primary-antibodies/map2-antibody-epr19691-ab183830.html.

    [98] [98] Chen X, Kandel ME, Shenghua H, et al. Artificial confocal microscopy for deep label-free imaging. Nat Photonics. 2022. .

    [100] [100] Anti-Ki67 Antibody [Ki-67] data sheet (PE) (A86642). Antibodies.com 2023. Accessed Oct 2023. https://www.antibodies.com/de/ki67-antibody-ki-67-pe-a86642.

    [101] [101] Xu ZD, Li X, Zhu XH, Chen LY, He YH, Chen YP. Effective Immunohistochemistry Pathology Microscopy Image Generation Using CycleGAN. Front Mol Biosci. 2020;7. .

    [105] [105] Gareau DS. Feasibility of digitally stained multimodal confocal mosaics to simulate histopathology. J Biomed Opt. 2009;14(3). .

    [106] [106] Bini J, Spain J, Nehal K, Hazelwood V, DiMarzio C, Rajadhyaksha M. Confocal mosaicing microscopy of basal cell carcinomas ex vivo: progress in digital staining to simulate histology-like appearance. Adv Biomed Clin Diagn Syst Ix. 2011;7890. .

    [107] [107] Bini J, Spain J, Nehal K, Hazelwood V, DiMarzio C, Rajadhyaksha M. Confocal mosaicing microscopy of human skin ex vivo: spectral analysis for digital staining to simulate histology-like appearance. J Biomed Opt. 2011;16(7). .

    [109] [109] Giacomelli MG, Husvogt L, Vardeh H, Faulkner-Jones BE, Hornegger J, Connolly JL, et al. Virtual Hematoxylin and Eosin Transillumination Microscopy Using Epi-Fluorescence Imaging. PLoS ONE. 2016;11(8). .

    [110] [110] Elfer KN, Sholl AB, Wang M, Tulman DB, Mandava H, Lee BR, et al. DRAQ5 and Eosin (‘D &E’) as an Analog to Hematoxylin and Eosin for Rapid Fluorescence Histology of Fresh Tissues. PLoS ONE. 2016;11(10). .

    [111] [111] Fan X, Tang ZY, Healy JJ, O’Dwyer K, Hennelly BM. Label-free Rheinberg staining of cells using digital holographic microscopy and spatial light interference microscopy. Adv Opt Imaging Technol Ii. 2019;11186. .

    [113] [113] Fan X, Healy JJ, O’Dwyer K, Hennelly BM. Label-free color staining of quantitative phase images. Opt Lasers Eng. 2020;129. .

    [115] [115] Bautista PA, Abe T, Yamaguchi M, Yagi Y, Ohyama N. Digital Staining of Pathological Images: Dye amount correction for improved classification performance. Med Imaging 2007 Comput-Aided Diagn Pts 1 2. 2007;6514. .

    [118] [118] Park Y, Park W, Jo Y, Min H, Cho H. Method and Apparatus for Generating 3D Fluorescent Label Image of Label-Free using 3D Refractive Index Tomography and Deep Learning. European Patent Application. 2021. Patent number: 11450062, Filed: March 19, 2020 Date of Patent: September 20, 2022.

    [121] [121] Segerer FJ, Nekolla K, Rognoni L, Kapil A, Schick M, Angell H, et al. Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI. In: Medical Imaging with Deep Learning. CoRR; 2022. .

    [123] [123] Shi L, Wong IH, Lo CT, Wong TT. One-side Virtual Histological Staining Model for Complex Human Samples. In: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece; 2022. p. 1–4..

    [126] [126] de Bel T, Hermse, M, Kers J, van der Laak J, Litjens G. Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology. In: Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research. 2019;102:151–163 Available from: https://proceedings.mlr.press/v102/de-bel19a.html.

    [128] [128] Cohen JP, Luck M, Honari S. Distribution matching losses can hallucinate features in medical image translation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Lecture Notes in Computer Science. vol. 11070. Cham: Springer. 2018. p. 529–36. .

    [129] [129] Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X. Improved techniques for training gans. Adv Neural Inf Process Syst. 2016;29. https://proceedings.neurips.cc/paper_files/paper/2016/hash/8a3363abe792db2d8761d6403605aeb7-Abstract.html.

    [130] [130] Durugkar I, Gemp I, Mahadevan S. Generative multi-adversarial networks. arXiv preprint arXiv:1611.01673. 2016.

    [131] [131] Frogner C, Zhang C, Mobahi H, Araya M, Poggio TA. Learning with a Wasserstein loss. Adv Neural Inf Process Syst. 2015;28. https://proceedings.neurips.cc/paper/2015/hash/a9eb812238f753132652ae09963a05e9-Abstract.html.

    [133] [133] Wang Z, Simoncelli EP, Bovik AC. Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003. Vol. 2. Pacific Grove; 2003. p. 1398-1402. .

    [135] [135] Bayramoglu N, Kaakinen M, Eklund L, Heikkila J. Towards Virtual H &E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks. In: 2017 IEEE International Conference on Computer Vision Workshops (Iccvw 2017). 2017. p. 64–71. .

    [137] [137] Trullo R, Bui QA, Tang Q, Olfati-Saber R. Image Translation Based Nuclei Segmentation for Immunohistochemistry Images. In: Mukhopadhyay A, Oksuz I, Engelhardt S, Zhu D, Yuan Y. (eds) Deep Generative Models. DGM4MICCAI 2022. Lecture Notes in Computer Science, vol 13609. Cham: Springer. .

    [138] [138] Bautista PA, Abe T, Yamaguchi M, Yagi Y, Ohyama N. Digital staining of pathological tissue specimens using spectral transmittance. Med Imaging 2005 Image Process Pt 1-3. 2005;5747:1892–1903. .

    [140] [140] Bautista PA, Yagi Y. Digital Staining for Histopathology Multispectral Images by the Combined Application of Spectral Enhancement and Spectral Transformation. In: 2011 Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc); 2011. p. 8013–8016. .

    [141] [141] Lotfollahi M, Daeinejad D, Berisha S, Mayerich D. Digital Staining of High-Resolution Ftir Spectroscopic Images. In: Appl Spectrosc. 2019;73(5):556–64. .

    [142] [142] Bulten W, Bándi P, Hoven J, Loo Rvd, Lotz J, Weiss N, et al. Epithelium segmentation using deep learning in H &E-stained prostate specimens with immunohistochemistry as reference standard. Sci Rep. 2019;9(1):1–10.

    [148] [148] Oszutowska-Mazurek D, Parafiniuk M, Mazurek P. Virtual UV Fluorescence Microscopy from Hematoxylin and Eosin Staining of Liver Images Using Deep Learning Convolutional Neural Network. Appl Sci-Basel. 2020;10(21). .

    [150] [150] Fredman G, Christensen RL, Ortner VK, Haedersdal M. Visualization of energy-based device-induced thermal tissue alterations using bimodal ex-vivo confocal microscopy with digital staining. A proof-of-concept study. In Skin Res Technol. 2022;28:564–70. .

    [151] [151] Meng XY, Li X, Wang X. A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks. Comput Math Methods Med. 2021;2021. .

    [154] [154] Liu K, Li B, Wu W, May C, Chang O, Knezevich S, et al. VSGD-Net: Virtual Staining Guided Melanocyte Detection on Histopathological Images. In: IEEE Winter Conf Appl Comput Vis. 2023;2023:1918–1927. .

    [155] [155] Ruini C, Vladimirova G, Kendziora B, Salzer S, Ergun E, Sattler E, et al. Ex-vivo fluorescence confocal microscopy with digital staining for characterizing basal cell carcinoma on frozen sections: A comparison with histology. J Biophotonics. 2021;14(8). .

    [156] [156] Kaza N, Ojaghi A, Costa PC, Robles FE. Deep learning based virtual staining of label-free ultraviolet (UV) microscopy images for hematological analysis. In: Label-free Biomedical Imaging and Sensing (LBIS) 2021. vol. 11655. Proceedings of the SPIE; 2021. p. 116550C. .

    [157] [157] Kaza N, Ojaghi A, Robles FE. Automated virtual staining, segmentation and classification of deep ultraviolet (UV) microscopy images for hematological analysis. In: Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper MW4A.5. .

    [158] [158] Ortner VK, Sahu A, Cordova M, Kose K, Aleissa S, Alessi-Fox C, et al. Exploring the utility of Deep Red Anthraquinone 5 for digital staining of ex vivo confocal micrographs of optically sectioned skin. J Biophotonics. 2021;14(4). .

    [161] [161] Cetin O, Chen M, Ziegler P, Wild P, Koeppl H. Deep learning-based restaining of histopathological images. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE Computer Society; 2022. p. 1467–1474.

    [165] [165] Özbey M, Dar SU, Bedel HA, Dalmaz O, Özturk Ş, Güngör A, et al. Unsupervised Medical Image Translation with Adversarial Diffusion Models. arXiv preprint arXiv:2207.08208. 2022.

    [166] [166] Guan H, Li D, Park Hc, Li A, Yue Y, Gau YA, et al. Deep-learning two-photon fiberscopy for video-rate brain imaging in freely-behaving mice. Nat Commun. 2022;13(1):1–9.

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    Lucas Kreiss, Shaowei Jiang, Xiang Li, Shiqi Xu, Kevin C. Zhou, Kyung Chul Lee, Alexander Mühlberg, Kanghyun Kim, Amey Chaware, Michael Ando, Laura Barisoni, Seung Ah Lee, Guoan Zheng, Kyle J. Lafata, Oliver Friedrich, Roarke Horstmeyer. Digital staining in optical microscopy using deep learning - a review[J]. PhotoniX, 2023, 4(1): 34

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    Paper Information

    Category: Research Articles

    Received: Apr. 26, 2023

    Accepted: Sep. 27, 2023

    Published Online: Dec. 14, 2023

    The Author Email: Kreiss Lucas (lucas.kreiss@duke.edu)

    DOI:10.1186/s43074-023-00113-4

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