1)Gupta A, Harrison PJ, Wieslander H, Pielawski N, Kartasalo K, et al: Deep learning in image cytometry: a review. Cytometry A 95: 366-380, 2019
2)Rivenson Y, Koydemir HC, Wang H, Wei Z, Ren Z, et al: Deep learning enhanced mobile-phone microscopy. ACS Photonics 5: 2354-2364, 2018
3)Zhang H, Fang C, Fei P: Deep-learning light-sheet fluorescence microscopy for high-throughput, voxel-super- resolved imaging of biomedical specimens. bioRxiv, October 04, 2018[doi: https://doi.org/10.1101/435040]
4)Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, et al: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318: 2199-2210, 2017
5)Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, et al: Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A 115: E2970-E2979, 2018
6)Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, et al: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLOS Med 16: e1002730, 2019[doi: 10.1371/journal.pmed.1002730]
7)Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, et al: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med 24: 1559-1567, 2018
8)Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, et al: Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep 9: 3358, 2019
9)Schaumberg AJ, Rubin MA, Fuchs TJ: H&E-stained whole slide image deep learning predicts SPOP mutation state in prostate cancer. bioRxiv, October 01, 2016[doi: https://doi.org/10.1101/064279]
10)Tainaka K, Kubota SI, Suyama TQ, Susaki EA, Perrin D, et al: Whole-body imaging with single-cell resolution by tissue decolorization. Cell 159: 911-924, 2014
11)Bedossa P, Dargère D, Paradis V: Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 38: 1449-1457, 2003
12)Onozato ML, Klepeis VE, Yagi Y, Mino-Kenudson M: A role of three-dimensional (3D)-reconstruction in the classification of lung adenocarcinoma. Anal Cell Pathol (Amst) 35: 79-84, 2012
13)Roberts N, Magee D, Song Y, Brabazon K, Shires M, et al: Toward routine use of 3D histopathology as a research tool. Am J Pathol 180: 1835-1842, 2012
14)Spalteholz W: Über das Durchsichtigmachen von menschlichen und tierischen Präparaten und seine theoretischen Bedingungen, nebst Anhang: Über Knochenfärbung. S. Hirzel, Leipzig, 1914
15)Dodt HU, Leischner U, Schierloh A, Jährling N, Mauch CP, et al: Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nat Methods 4: 331-336, 2007
16)Mano T, Albanese A, Dodt HU, Erturk A, Gradinaru V, et al: Whole-brain analysis of cells and circuits by tissue clearing and light-sheet microscopy. J Neurosci 38: 9330-9337, 2018
17)Dent JA, Polson AG, Klymkowsky MW: A whole-mount immunocytochemical analysis of the expression of the intermediate filament protein vimentin in Xenopus. Development 105: 61-74, 1989
18)Ertürk A, Becker K, Jährling N, Mauch CP, Hojer CD, et al: Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat Protoc 7: 1983-1995, 2012
19)Pan C, Cai R, Quacquarelli FP, Ghasemigharagoz A, Lourbopoulos A, et al: Shrinkage-mediated imaging of entire organs and organisms using uDISCO. Nat Methods 13: 859-867, 2016
20)Klingberg A, Hasenberg A, Ludwig-Portugall I, Medyukhina A, Männ L, et al: Fully automated evaluation of total glomerular number and capillary tuft size in nephritic kidneys using lightsheet microscopy. J Am Soc Nephrol 28: 452-459, 2017
21)Tuchin VV, Bashkatov AN, Genina EA, Kochubey VI, Lakodina NA, et al: Optics of living tissues with controlled scattering properties. Proc SPIE 3863: 10-21, 1999
22)Chiang AS, Liu YC, Chiu SL, Hu SH, Huang CH, et al: Three‐dimensional mapping of brain neuropils in the cockroach, Diploptera punctata. J Comp Neurol 440: 1-11, 2001
23)Hama H, Hioki H, Namiki K, Hoshida T, Kurokawa H, et al: ScaleS: an optical clearing palette for biological imaging. Nat Neurosci 18: 1518-1529, 2015
24)Hama H, Kurokawa H, Kawano H, Ando R, Shimogori T, et al: Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat Neurosci 14: 1481-1488, 2011
25)Susaki EA, Tainaka K, Perrin D, Kishino F, Tawara T, et al: Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157: 726-739, 2014
26)Tainaka K, Murakami TC, Susaki EA, Shimizu C, Saito R, et al: Chemical landscape for tissue clearing based on hydrophilic reagents. Cell Rep 24: 2196-2210, e9, 2018
27)Chung K, Wallace J, Kim SY, Kalyanasundaram S, Andalman AS, et al: Structural and molecular interrogation of intact biological systems. Nature 497: 332-337, 2013
28)Gradinaru V, Treweek J, Overton K, Deisseroth K: Hydrogel-tissue chemistry: principles and applications. Annu Rev Biophys 47: 355-376, 2018
29)Yang B, Treweek JB, Kulkarni RP, Deverman BE, Chen CK, et al: Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158: 945-958, 2014
30)Murray E, Cho JH, Goodwin D, Ku T, Swaney J, et al: Simple, scalable proteomic imaging for high-dimensional profiling of intact systems. Cell 163: 1500-1514, 2015
31)Park YG, Sohn CH, Chen R, McCue M, Yun DH, et al: Protection of tissue physicochemical properties using polyfunctional crosslinkers. Nat Biotechnol, Dec 17, 2018[doi: 10.1038/nbt.4281]
32)Pitrone PG, Schindelin J, Stuyvenberg L, Preibisch S, Weber M, et al: OpenSPIM: an open-access light-sheet microscopy platform. Nat Methods 10: 598-599, 2013
33)Voigt FF, Kirschenbaum D, Platonova E, Pagès S, Campbell RAA, et al: The mesoSPIM initiative: open-source light-sheet mesoscopes for imaging in cleared tissue. bioRxiv, March 18, 2019[doi: https://doi.org/10.1101/577122]
34)Glaser AK, Reder NP, Chen Y, McCarty EF, Yin C, et al: Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat Biomed Eng 1: pii: 0084, 2017[doi: 10.1038/s41551-017-0084]
35)Kubota SI, Takahashi K, Nishida J, Morishita Y, Ehata S, et al: Whole-body profiling of cancer metastasis with single-cell resolution. Cell Rep 20: 236-250, 2017
36)Susaki EA, Tainaka K, Perrin D, Yukinaga H, Kuno A, et al: Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging. Nat Protoc 10: 1709-1727, 2015
37)Nojima S, Susaki EA, Yoshida K, Takemoto H, Tsujimura N, et al: CUBIC pathology: three-dimensional imaging for pathological diagnosis. Sci Rep 7: 9269, 2017[doi: 10.1038/s41598-017-09117-0]
38)Pan C, Schoppe O, Parra-Damas A, Cai R, Todorov MI, et al: Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body. bioRxiv, February 05, 2019[doi: https://doi.org/10.1101/541862]
39)Cai R, Pan C, Ghasemigharagoz A, Todorov MI, Förstera B, et al: Panoptic imaging of transparent mice reveals whole-body neuronal projections and skull-meninges connections. Nat Neurosci 22: 317-327, 2019
40)Ando K, Laborde Q, Lazar A, Godefroy D, Youssef I, et al: Inside Alzheimer brain with CLARITY: senile plaques, neurofibrillary tangles and axons in 3-D. Acta neuropathol 128: 457-459, 2014
41)Lai HM, Liu AKL, Ng HHM, Goldfinger MH, Chau TW, et al: Next generation histology methods for three-dimensional imaging of fresh and archival human brain tissues. Nat Commun 9: 1066, 2018
42)Hildebrand S, Schueth A, Herrler A, Galuske R, Roebroeck A: Scalable cytoarchitectonic characterization of large intact human neocortex samples. bioRxiv, March 05, 2018[doi: https://doi.org/10.1101/274985]
43)Belle M, Godefroy D, Couly G, Malone SA, Collier F, et al: Tridimensional visualization and analysis of early human development. Cell 169: 161-173, e12, 2017[doi: 10.1016/j.cell.2017.03.008]
44)Chen Y, Shen Q, White SL, Gokmen-Polar Y, Badve S, et al: Three-dimensional imaging and quantitative analysis in CLARITY processed breast cancer tissues. Sci Rep 9: 5624, 2019
45)van Royen ME, Verhoef EI, Kweldam CF, van Cappellen WA, Kremers GJ, et al: Three-dimensional microscopic analysis of clinical prostate specimens. Histopathology 69: 985-992, 2016
46)Todorov MI, Paetzold JC, Schoppe O, Tetteh G, Efremov V, et al: Automated analysis of whole brain vasculature using machine learning. bioRxiv, April 18, 2019[doi: https://doi.org/10.1101/613257]
47)McQuin C, Goodman A, Chernyshev V, Kamentsky L, Cimini BA, et al: CellProfiler 3.0: next-generation image processing for biology. PLOS Biol 16: e2005970, 2018[doi: 10.1371/journal.pbio.2005970]
48)Sommer C, Straehle C, Köthe U, Hamprecht FA: Ilastik: interactive learning and segmentation toolkit. 2011 IEEE International Symposium on Biomedical Imaging From Nano to Macro, 230-233, 2011
49)Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, et al: Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33: 2424-2426, 2017