• Amunts K et al (2013) BigBrain: an ultrahigh-resolution 3D human brain model. Science 340(6139):1472–1475

    Article 

    Google Scholar
     

  • Amunts K et al (2020) Julich-Brain: a 3D probabilistic atlas of the human brain’s cytoarchitecture. Science 369(6506):988–992

    Article 

    Google Scholar
     

  • Gouwens NW et al (2019) Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat Neurosci 22(7):1182–1195

    Article 

    Google Scholar
     

  • Tasic B et al (2018) Shared and distinct transcriptomic cell types across neocortical areas. Nature 563(7729):72–78

    Article 

    Google Scholar
     

  • Alivisatos AP et al (2012) The brain activity map project and the challenge of functional connectomics. Neuron 74(6):970–974

    Article 

    Google Scholar
     

  • Ecker JR et al (2017) The BRAIN initiative cell census consortium: lessons learned toward generating a comprehensive brain cell atlas. Neuron 96(3):542–557

    Article 

    Google Scholar
     

  • Kandel ER et al (2013) Neuroscience thinks big (and collaboratively). Nat Rev Neurosci 14(9):659–664

    Article 

    Google Scholar
     

  • Poo M-M et al (2016) China brain project: basic neuroscience, brain diseases, and brain-inspired computing. Neuron 92(3):591–596

    Article 

    Google Scholar
     

  • Rotolo T et al (2008) Genetically-directed, cell type-specific sparse labeling for the analysis of neuronal morphology. PLoS ONE 3(12):e4099

    Article 

    Google Scholar
     

  • Madisen L et al (2015) Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance. Neuron 85(5):942–958

    Article 

    Google Scholar
     

  • Graybuck LT et al (2021) Enhancer viruses for combinatorial cell-subclass-specific labeling. Neuron 109(9):1449-1464.e13

    Article 

    Google Scholar
     

  • Ertürk A et al (2012) Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat Protoc 7(11):1983–1995

    Article 

    Google Scholar
     

  • Murakami TC et al (2018) A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing. Nat Neurosci 21(4):625–637

    Article 

    Google Scholar
     

  • Ueda HR et al (2020) Whole-brain profiling of cells and circuits in mammals by tissue clearing and light-sheet microscopy. Neuron 106(3):369–387

    Article 

    Google Scholar
     

  • Economo MN et al (2016) A platform for brain-wide imaging and reconstruction of individual neurons. Elife 5:e10566

    Article 

    Google Scholar
     

  • Gong H et al (2016) High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nat Commun 7(1):1–12


    Google Scholar
     

  • Xu F et al (2021) High-throughput mapping of a whole rhesus monkey brain at micrometer resolution. Nat Biotechnol 39(12):1521–1528

    Article 

    Google Scholar
     

  • Osten P, Margrie TW (2013) Mapping brain circuitry with a light microscope. Nat Methods 10(6):515–523

    Article 

    Google Scholar
     

  • Winnubst J et al (2019) 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

    Article 

    Google Scholar
     

  • Peng H et al (2015) BigNeuron: large-scale 3D neuron reconstruction from optical microscopy images. Neuron 87(2):252–256

    Article 

    Google Scholar
     

  • Belmonte JCI et al (2015) Brains, genes, and primates. Neuron 86(3):617–631

    Article 

    Google Scholar
     

  • Bakken TE et al (2021) Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598(7879):111–119

    Article 

    Google Scholar
     

  • DeFelipe J (2015) The anatomical problem posed by brain complexity and size: a potential solution. Front Neuroanat 9:104


    Google Scholar
     

  • Lin MK et al (2019) A high-throughput neurohistological pipeline for brain-wide mesoscale connectivity mapping of the common marmoset. Elife 8:e40042

    Article 

    Google Scholar
     

  • Yang B et al (2014) Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158(4):945–958

    Article 

    Google Scholar
     

  • Susaki EA et al (2014) Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157(3):726–739

    Article 

    Google Scholar
     

  • Zhao S et al (2020) Cellular and molecular probing of intact human organs. Cell 180(4):796-812.e19

    Article 

    Google Scholar
     

  • Ragan T et al (2012) Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat Methods 9(3):255–258

    Article 

    Google Scholar
     

  • Li A et al (2010) Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science 330(6009):1404–1408

    Article 

    Google Scholar
     

  • Zheng T et al (2013) Visualization of brain circuits using two-photon fluorescence micro-optical sectioning tomography. Opt Express 21(8):9839–9850

    Article 

    Google Scholar
     

  • Haberl MG et al (2015) An anterograde rabies virus vector for high-resolution large-scale reconstruction of 3D neuron morphology. Brain Struct Funct 220(3):1369–1379

    Article 

    Google Scholar
     

  • Wouterlood FG et al (2014) A fourth generation of neuroanatomical tracing techniques: exploiting the offspring of genetic engineering. J Neurosci Methods 235(1):331–348

    Article 

    Google Scholar
     

  • Veldman MB et al (2020) Brainwide genetic sparse cell labeling to illuminate the morphology of neurons and glia with cre-dependent MORF mice. Neuron 108(1):111-127.e6

    Article 

    Google Scholar
     

  • Ibrahim LA et al (2021) Sparse labeling and neural tracing in brain circuits by STARS strategy: revealing morphological development of type II spiral ganglion neurons. Cereb Cortex 31(5):2759–2772

    Article 

    Google Scholar
     

  • Cai D et al (2013) Improved tools for the Brainbow toolbox. Nat Methods 10(6):540–547

    Article 

    Google Scholar
     

  • Kobbert C et al (2000) Current concepts in neuroanatomical tracing. Prog Neurobiol 62(4):327–351

    Article 

    Google Scholar
     

  • Long B et al (2015) 3D image-guided automatic pipette positioning for single cell experiments in vivo. Sci Rep 5(1):1–8


    Google Scholar
     

  • Wu Q, Chubykin AA (2017) Application of automated image-guided patch clamp for the study of neurons in brain slices. JoVE 125:e56010


    Google Scholar
     

  • Holst GL et al (2019) Autonomous patch clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex. J Neurophysiol 121(6):2341–2357

    Article 

    Google Scholar
     

  • Koos K et al (2021) Automatic deep learning-driven label-free image-guided patch clamp system. Nat Commun 12(1):936

    Article 

    Google Scholar
     

  • Bürgers J et al (2019) Light-sheet fluorescence expansion microscopy: fast mapping of neural circuits at super resolution. Neurophotonics 6(1):015005

    Article 

    Google Scholar
     

  • Wassie AT, Zhao Y, Boyden ES (2019) Expansion microscopy: principles and uses in biological research. Nat Methods 16(1):33–41

    Article 

    Google Scholar
     

  • Chen F, Tillberg PW, Boyden ES (2015) Optical imaging. Expansion microscopy. Science 347(6221):543–548

    Article 

    Google Scholar
     

  • Weiss KR et al (2021) Tutorial: practical considerations for tissue clearing and imaging. Nat Protoc 16(6):2732–2748

    Article 

    Google Scholar
     

  • Tichauer KM et al (2015) Quantitative in vivo cell-surface receptor imaging in oncology: kinetic modeling and paired-agent principles from nuclear medicine and optical imaging. Phys Med Biol 60(14):R239

    Article 

    Google Scholar
     

  • Balas C (2009) Review of biomedical optical imaging—a powerful, non-invasive, non-ionizing technology for improving in vivo diagnosis. Meas Sci Technol 20(10):104020

    Article 

    Google Scholar
     

  • Luker GD, Luker KE (2008) Optical imaging: current applications and future directions. J Nucl Med 49(1):1–4

    Article 

    Google Scholar
     

  • Fiolka R et al (2012) Time-lapse two-color 3D imaging of live cells with doubled resolution using structured illumination. Proc Natl Acad Sci 109(14):5311–5315

    Article 

    Google Scholar
     

  • Zong W et al (2015) Large-field high-resolution two-photon digital scanned light-sheet microscopy. Cell Res 25(2):254–257

    Article 

    Google Scholar
     

  • Chen B-C et al (2014) Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346(6208):1257998

    Article 

    Google Scholar
     

  • Tsai Y-C et al (2020) Rapid high resolution 3D imaging of expanded biological specimens with lattice light sheet microscopy. Methods 174:11–19

    Article 

    Google Scholar
     

  • Truong TV et al (2011) Deep and fast live imaging with two-photon scanned light-sheet microscopy. Nat Methods 8(9):757–760

    Article 

    Google Scholar
     

  • Olivier N et al (2009) Two-photon microscopy with simultaneous standard and extended depth of field using a tunable acoustic gradient-index lens. Opt Lett 34(11):1684–1686

    Article 

    Google Scholar
     

  • Dufour P et al (2006) Two-photon excitation fluorescence microscopy with a high depth of field using an axicon. Appl Opt 45(36):9246–9252

    Article 

    Google Scholar
     

  • Ji N (2017) Adaptive optical fluorescence microscopy. Nat Methods 14(4):374–380

    Article 

    Google Scholar
     

  • Logan SL et al (2018) Automated high-throughput light-sheet fluorescence microscopy of larval zebrafish. PLoS ONE 13(11):e0198705

    Article 

    Google Scholar
     

  • Sala F et al (2020) High-throughput 3D imaging of single cells with light-sheet fluorescence microscopy on chip. Biomed Opt Express 11(8):4397–4407

    Article 

    Google Scholar
     

  • Govindan S et al (2021) Mass generation, neuron labeling, and 3D imaging of minibrains. Front Bioeng Biotechnol 8:1436

    Article 

    Google Scholar
     

  • Szalay G et al (2016) Fast 3D imaging of spine, dendritic, and neuronal assemblies in behaving animals. Neuron 92(4):723–738

    Article 

    Google Scholar
     

  • Nikolenko V et al (2008) SLM microscopy: scanless two-photon imaging and photostimulation using spatial light modulators. Front Neural Circuits 2:5

    Article 

    Google Scholar
     

  • Lindell DB, O’Toole M, Wetzstein G (2018) Single-photon 3D imaging with deep sensor fusion. ACM Trans Graph 37(4):113-1-113–12

    Article 

    Google Scholar
     

  • Griffiths VA et al (2020) Real-time 3D movement correction for two-photon imaging in behaving animals. Nat Methods 17(7):741–748

    Article 

    Google Scholar
     

  • Power RM, Huisken J (2018) Adaptable, illumination patterning light sheet microscopy. Sci Rep 8(1):1–11


    Google Scholar
     

  • Štefko M et al (2018) Autonomous illumination control for localization microscopy. Opt Express 26(23):30882–30900

    Article 

    Google Scholar
     

  • Hubert A et al (2019) Adaptive optics light-sheet microscopy based on direct wavefront sensing without any guide star. Opt Lett 44(10):2514–2517

    Article 

    Google Scholar
     

  • Wilding D et al (2016) Adaptive illumination based on direct wavefront sensing in a light-sheet fluorescence microscope. Opt Express 24(22):24896–24906

    Article 

    Google Scholar
     

  • Durand A et al (2018) A machine learning approach for online automated optimization of super-resolution optical microscopy. Nat Commun 9(1):1–16

    Article 

    Google Scholar
     

  • Royer LA et al (2016) Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms. Nat Biotechnol 34(12):1267–1278

    Article 

    Google Scholar
     

  • Fang C et al (2021) Minutes-timescale 3D isotropic imaging of entire organs at subcellular resolution by content-aware compressed-sensing light-sheet microscopy. Nat Commun 12(1):1–13

    Article 

    Google Scholar
     

  • Chen H et al (2021) Sparse imaging and reconstruction tomography for high-speed high-resolution whole-brain imaging. Cell Rep Methods 1(6):100089

    Article 

    Google Scholar
     

  • Long B et al (2017) SmartScope2: simultaneous imaging and reconstruction of neuronal morphology. Sci Rep 7(1):1–7

    Article 

    Google Scholar
     

  • He J, Huisken J (2020) Image quality guided smart rotation improves coverage in microscopy. Nat Commun 11(1):1–9

    Article 

    Google Scholar
     

  • Paddock SW (1999) Confocal laser scanning microscopy. Biotechniques 27(5):992–1004

    Article 

    Google Scholar
     

  • Gräf R, Rietdorf J, Zimmermann T (2005) Live cell spinning disk microscopy. Microsc Tech 95:57–75

    Article 

    Google Scholar
     

  • Stehbens S et al (2012) Imaging intracellular protein dynamics by spinning disk confocal microscopy. Methods Enzymol 504:293–313

    Article 

    Google Scholar
     

  • Denk W, Strickler JH, Webb WW (1990) Two-photon laser scanning fluorescence microscopy. Science 248(4951):73–76

    Article 

    Google Scholar
     

  • Weber M, Huisken J (2011) Light sheet microscopy for real-time developmental biology. Curr Opin Genet Dev 21(5):566–572

    Article 

    Google Scholar
     

  • Huisken J et al (2004) Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305(5686):1007–1009

    Article 

    Google Scholar
     

  • Tomer R et al (2012) Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy. Nat Methods 9(7):755–763

    Article 

    Google Scholar
     

  • McDole K et al (2018) In toto imaging and reconstruction of post-implantation mouse development at the single-cell level. Cell 175(3):859-876.e33

    Article 

    Google Scholar
     

  • Levoy M et al (2006) Light field microscopy. In: ACM SIGGRAPH 2006 Papers. pp 924–934

  • Prevedel R et al (2014) Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nat Methods 11(7):727–730

    Article 

    Google Scholar
     

  • Choquet D, Sainlos M, Sibarita J-B (2021) Advanced imaging and labelling methods to decipher brain cell organization and function. Nat Rev Neurosci 22(4):237–255

    Article 

    Google Scholar
     

  • Gong H et al (2013) Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution. Neuroimage 74:87–98

    Article 

    Google Scholar
     

  • Zhong Q et al (2021) High-definition imaging using line-illumination modulation microscopy. Nat Methods 18(3):309–315

    Article 

    Google Scholar
     

  • Narasimhan A et al (2017) Oblique light-sheet tomography: fast and high resolution volumetric imaging of mouse brains. BioRxiv. https://doi.org/10.1101/132423

    Article 

    Google Scholar
     

  • Yang X et al (2018) High-throughput light sheet tomography platform for automated fast imaging of whole mouse brain. J Biophotonics 11(9):e201800047

    Article 

    Google Scholar
     

  • Zhang Z et al (2021) Multi-scale light-sheet fluorescence microscopy for fast whole brain imaging. Front Neuroanat. https://doi.org/10.3389/fnana.2021.732464

    Article 

    Google Scholar
     

  • Kashekodi AB et al (2018) Miniature scanning light-sheet illumination implemented in a conventional microscope. Biomed Opt Express 9(9):4263–4274

    Article 

    Google Scholar
     

  • Dabov K et al (2006) Image denoising with block-matching and 3D filtering. Image processing: algorithms and systems, neural networks, and machine learning. International Society for Optics and Photonics, Bellingham


    Google Scholar
     

  • Buades A, Coll B, Morel J-M (2005) A non-local algorithm for image denoising. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). IEEE

  • Xu J, Zhang L, Zhang D (2018) A trilateral weighted sparse coding scheme for real-world image denoising. In: Proceedings of the European conference on computer vision (ECCV)

  • Smith K et al (2015) CIDRE: an illumination-correction method for optical microscopy. Nat Methods 12(5):404–406

    Article 

    Google Scholar
     

  • Chernavskaia O et al (2017) Correction of mosaicking artifacts in multimodal images caused by uneven illumination. J Chemom 31(6):e2901

    Article 

    Google Scholar
     

  • Rahman S et al (2016) An adaptive gamma correction for image enhancement. EURASIP J Image Video Process 2016(1):1–13

    Article 

    Google Scholar
     

  • Peng T et al (2017) A BaSiC tool for background and shading correction of optical microscopy images. Nat Commun 8(1):1–7

    Article 

    Google Scholar
     

  • Becker K et al (2019) Deconvolution of light sheet microscopy recordings. Sci Rep 9(1):1–14


    Google Scholar
     

  • Preibisch S et al (2014) Efficient Bayesian-based multiview deconvolution. Nat Methods 11(6):645–648

    Article 

    Google Scholar
     

  • Zhao W et al (2021) Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nat Biotechnol 40:606–617

    Article 

    Google Scholar
     

  • Hayman M et al (2004) Enhanced neurite outgrowth by human neurons grown on solid three-dimensional scaffolds. Biochem Biophys Res Commun 314(2):483–488

    Article 

    Google Scholar
     

  • Li Q, Sone S, Doi K (2003) Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans. Med Phys 30(8):2040–2051

    Article 

    Google Scholar
     

  • Zhou Z et al (2015) Adaptive image enhancement for tracing 3D morphologies of neurons and brain vasculatures. Neuroinformatics 13(2):153–166

    Article 

    Google Scholar
     

  • Mukherjee S, Acton ST (2015) Oriented filters for vessel contrast enhancement with local directional evidence. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI). IEEE

  • Liang H, Acton ST, Weller DS (2017) Content-aware neuron image enhancement. In: 2017 IEEE international conference on image processing (ICIP). IEEE.

  • Guo S et al (2022) Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons. Bioinformatics 38(2):503–512

    Article 

    Google Scholar
     

  • Bria A, Iannello G (2012) TeraStitcher-a tool for fast automatic 3D-stitching of teravoxel-sized microscopy images. BMC Bioinform 13(1):1–15

    Article 

    Google Scholar
     

  • Hörl D et al (2019) BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat Methods 16(9):870–874

    Article 

    Google Scholar
     

  • Hayworth KJ et al (2015) Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics. Nat Methods 12(4):319–322

    Article 

    Google Scholar
     

  • Chen H et al (2017) Fast assembling of neuron fragments in serial 3D sections. Brain Inform 4(3):183–186

    Article 

    Google Scholar
     

  • Li Y et al (2017) TDat: an efficient platform for processing petabyte-scale whole-brain volumetric images. Front Neural Circuits 11:51

    Article 

    Google Scholar
     

  • Bria A et al (2016) TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images. Nat Methods 13(3):192–194

    Article 

    Google Scholar
     

  • Pietzsch T et al (2015) BigDataViewer: visualization and processing for large image data sets. Nat Methods 12(6):481–483

    Article 

    Google Scholar
     

  • Wang Q et al (2020) The Allen mouse brain common coordinate framework: a 3D reference atlas. Cell 181(4):936-953.e20

    Article 

    Google Scholar
     

  • Niedworok CJ et al (2016) aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data. Nat Commun 7(1):1–9

    Article 

    Google Scholar
     

  • Renier N et al (2016) Mapping of brain activity by automated volume analysis of immediate early genes. Cell 165(7):1789–1802

    Article 

    Google Scholar
     

  • Kim Y et al (2017) Brain-wide maps reveal stereotyped cell-type-based cortical architecture and subcortical sexual dimorphism. Cell 171(2):456-469.e22

    Article 

    Google Scholar
     

  • Fürth D et al (2018) An interactive framework for whole-brain maps at cellular resolution. Nat Neurosci 21(1):139–149

    Article 

    Google Scholar
     

  • Ni H et al (2020) A robust image registration interface for large volume brain atlas. Sci Rep 10(1):1–16

    Article 

    Google Scholar
     

  • Arganda-Carreras I et al (2008) bunwarpj: consistent and elastic registration in imagej, methods and applications. In: Second imageJ user & developer conference

  • Qu L et al (2022) Cross-modal coherent registration of whole mouse brains. Nat Methods 19(1):111–118

    Article 

    Google Scholar
     

  • Donohue DE, Ascoli GA (2011) Automated reconstruction of neuronal morphology: an overview. Brain Res Rev 67(1–2):94–102

    Article 

    Google Scholar
     

  • Svoboda K (2011) The past, present, and future of single neuron reconstruction. Neuroinformatics 9(2–3):97

    Article 

    Google Scholar
     

  • Acciai L, Soda P, Iannello G (2016) Automated neuron tracing methods: an updated account. Neuroinformatics 14(4):353–367

    Article 

    Google Scholar
     

  • Gala R et al (2017) Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images. Elife 6:e29315

    Article 

    Google Scholar
     

  • Tyson AL et al (2021) A deep learning algorithm for 3D cell detection in whole mouse brain image datasets. PLoS Comput Biol 17(5):e1009074

    Article 

    Google Scholar
     

  • Ascoli GA (2008) Neuroinformatics grand challenges. Neuroinformatics 6(1):1–3

    MathSciNet 
    Article 

    Google Scholar
     

  • Yuan X et al (2009) MDL constrained 3-D grayscale skeletonization algorithm for automated extraction of dendrites and spines from fluorescence confocal images. Neuroinformatics 7(4):213–232

    Article 

    Google Scholar
     

  • Lee PC et al (2012) High-throughput computer method for 3D neuronal structure reconstruction from the image stack of the Drosophila brain and its applications. PLoS Comput Biol 8(9):e1002658

    Article 

    Google Scholar
     

  • Gala R et al (2014) Active learning of neuron morphology for accurate automated tracing of neurites. Front Neuroanat 8:37

    Article 

    Google Scholar
     

  • Xiao H, Peng HJB (2013) APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree. Bioinformatics 29(11):1448–1454

    Article 

    Google Scholar
     

  • Wang Y et al (2011) A broadly applicable 3-D neuron tracing method based on open-curve snake. Neuroinformatics 9(2):193–217

    Article 

    Google Scholar
     

  • Chen H et al (2015) SmartTracing: self-learning-based neuron reconstruction. Brain Inform 2(3):135–144

    Article 

    Google Scholar
     

  • Yang J, Gonzalez-Bellido PT, Peng H (2013) A distance-field based automatic neuron tracing method. BMC Bioinform 14(1):1–11

    Article 

    Google Scholar
     

  • Zhao T et al (2011) Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics 9(2):247–261

    Article 

    Google Scholar
     

  • Choromanska A, Chang S-F, Yuste R (2012) Automatic reconstruction of neural morphologies with multi-scale tracking. Front Neural Circuits 6:25

    Article 

    Google Scholar
     

  • Zhou Z et al (2016) TReMAP: automatic 3D neuron reconstruction based on tracing, reverse mapping and assembling of 2D projections. Neuroinformatics 14(1):41–50

    Article 

    Google Scholar
     

  • Peng H et al (2017) Automatic tracing of ultra-volumes of neuronal images. Nat Methods 14(4):332–333

    Article 

    Google Scholar
     

  • Zhou H et al (2021) GTree: an open-source tool for dense reconstruction of brain-wide neuronal population. Neuroinformatics 19(2):305–317

    Article 

    Google Scholar
     

  • Yang J et al (2019) FMST: an automatic neuron tracing method based on fast marching and minimum spanning tree. Neuroinformatics 17(2):185–196

    Article 

    Google Scholar
     

  • Liu S et al (2016) Rivulet: 3d neuron morphology tracing with iterative back-tracking. Neuroinformatics 14(4):387–401

    Article 

    Google Scholar
     

  • Peng H, Long F, Myers GJB (2011) Automatic 3D neuron tracing using all-path pruning. Bioinformatics 27(13):i239–i247

    Article 

    Google Scholar
     

  • Mukherjee S, Condron B, Acton ST (2014) Tubularity flow field—a technique for automatic neuron segmentation. IEEE Trans Image Process 24(1):374–389

    MathSciNet 
    MATH 
    Article 

    Google Scholar
     

  • DeFelipe J et al (2013) New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat Rev Neurosci 14(3):202–216

    Article 

    Google Scholar
     

  • Jiang X et al (2015) Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350(6264):aac9462

    Article 

    Google Scholar
     

  • Zeng H, Sanes JR (2017) Neuronal cell-type classification: challenges, opportunities and the path forward. Nat Rev Neurosci 18(9):530–546

    Article 

    Google Scholar
     

  • Yang J, He Y, Liu X (2020) Retrieving similar substructures on 3D neuron reconstructions. Brain Inform 7(1):1–9

    Article 

    Google Scholar
     

  • Wan Y et al (2015) BlastNeuron for automated comparison, retrieval and clustering of 3D neuron morphologies. Neuroinformatics 13(4):487–499

    Article 

    Google Scholar
     

  • Li Y et al (2017) Metrics for comparing neuronal tree shapes based on persistent homology. PLoS ONE 12(8):e0182184

    Article 

    Google Scholar
     

  • Sholl D (1953) Dendritic organization in the neurons of the visual and motor cortices of the cat. J Anat 87(Pt 4):387


    Google Scholar
     

  • Zhao T, Plaza SM (2014) Automatic neuron type identification by neurite localization in the drosophila medulla. arXiv preprint arXiv:1409.1892

  • McInnes L, Healy J, Melville J (2018) UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426

  • Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recogn 36(2):451–461

    Article 

    Google Scholar
     

  • Johnson SC (1967) Hierarchical clustering schemes. Psychometrika 32(3):241–254

    MATH 
    Article 

    Google Scholar
     

  • Scorcioni R, Polavaram S, Ascoli GA (2008) L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat Protoc 3(5):866–876

    Article 

    Google Scholar
     

  • Peng H et al (2021) Morphological diversity of single neurons in molecularly defined cell types. Nature 598(7879):174–181

    Article 

    Google Scholar
     

  • Mihaljević B et al (2015) Bayesian network classifiers for categorizing cortical GABAergic interneurons. Neuroinformatics 13(2):193–208

    Article 

    Google Scholar
     

  • Santana R et al (2013) Classification of neocortical interneurons using affinity propagation. Front Neural Circuits 7:185

    Article 

    Google Scholar
     

  • Sümbül U et al (2014) A genetic and computational approach to structurally classify neuronal types. Nat Commun 5(1):1–12


    Google Scholar
     

  • Gillette TA, Ascoli GA (2015) Topological characterization of neuronal arbor morphology via sequence representation: I-motif analysis. BMC Bioinform 16(1):1–15

    Article 

    Google Scholar
     

  • Gillette TA, Hosseini P, Ascoli GA (2015) Topological characterization of neuronal arbor morphology via sequence representation: II-global alignment. BMC Bioinform 16(1):1–17

    Article 

    Google Scholar
     

  • Network BICC (2021) A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598(7879):86–102

    Article 

    Google Scholar
     

  • Shrestha A, Mahmood A (2019) Review of deep learning algorithms and architectures. IEEE Access 7:53040–53065

    Article 

    Google Scholar
     

  • Eliceiri KW et al (2012) Biological imaging software tools. Nat Methods 9(7):697–710

    Article 

    Google Scholar
     

  • Mosaliganti KR et al (2012) ACME: automated cell morphology extractor for comprehensive reconstruction of cell membranes. PLoS Comput Biol 8(12):e1002780

    Article 

    Google Scholar
     

  • Piccinini F et al (2017) Advanced cell classifier: user-friendly machine-learning-based software for discovering phenotypes in high-content imaging data. Cell Syst 4(6):651-655.e5

    Article 

    Google Scholar
     

  • Stegmaier J et al (2016) Real-time three-dimensional cell segmentation in large-scale microscopy data of developing embryos. Dev Cell 36(2):225–240

    Article 

    Google Scholar
     

  • Fernandez R et al (2010) Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nat Methods 7(7):547–553

    Article 

    Google Scholar
     

  • Carpenter AE et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7(10):1–11

    Article 

    Google Scholar
     

  • McQuin C et al (2018) CellProfiler 3.0: next-generation image processing for biology. PLoS Biol 16(7):e2005970

    Article 

    Google Scholar
     

  • Stalling D, Westerhoff M, Hege HC (2005) Amira: a highly interactive system for visual data analysis. The visualization handbook. Elsevier Inc., Amsterdam, pp 749–767

    Chapter 

    Google Scholar
     

  • Glaser JR, Glaser EM (1990) Neuron imaging with neurolucida—a PC-based system for image combining microscopy. Comput Med Imaging Graph 14(5):307–317

    Article 

    Google Scholar
     

  • Cuntz H et al (2011) The TREES toolbox—probing the basis of axonal and dendritic branching. Neuroinformatics 9(1):91–96

    Article 

    Google Scholar
     

  • Bates AS et al (2020) The natverse, a versatile toolbox for combining and analysing neuroanatomical data. Elife 9:e53350

    Article 

    Google Scholar
     

  • Peng H et al (2010) V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat Biotechnol 28(4):348–353

    Article 

    Google Scholar
     

  • Wang Y et al (2019) TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain. Nat Commun 10(1):1–9

    Article 

    Google Scholar
     

  • Allan C et al (2012) OMERO: flexible, model-driven data management for experimental biology. Nat Methods 9(3):245–253

    Article 

    Google Scholar
     

  • Kvilekval K et al (2010) Bisque: a platform for bioimage analysis and management. Bioinformatics 26(4):544–552

    Article 

    Google Scholar
     

  • Peng H et al (2014) Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis. Nat Commun 5(1):1–13


    Google Scholar
     

  • Jiang S et al (2022) Petabyte-scale multi-morphometry of single neurons for whole brains. Neuroinformatics

  • Schindelin J et al (2015) The ImageJ ecosystem: an open platform for biomedical image analysis. Mol Reprod Dev 82(7–8):518–529

    Article 

    Google Scholar
     

  • Kankaanpää P et al (2012) BioImageXD: an open, general-purpose and high-throughput image-processing platform. Nat Methods 9(7):683–689

    Article 

    Google Scholar
     

  • De Chaumont F et al (2012) Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods 9(7):690–696

    Article 

    Google Scholar
     

  • Wan Y et al (2012) FluoRender: an application of 2D image space methods for 3D and 4D confocal microscopy data visualization in neurobiology research. In: 2012 IEEE pacific visualization symposium. IEEE

  • Arshadi C et al (2021) SNT: a unifying toolbox for quantification of neuronal anatomy. Nat Methods 18(4):374–377

    Article 

    Google Scholar
     

  • Sunkin SM et al (2012) Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res 41(D1):D996–D1008

    Article 

    Google Scholar
     

  • Mikula S et al (2007) Internet-enabled high-resolution brain mapping and virtual microscopy. Neuroimage 35(1):9–15

    MathSciNet 
    Article 

    Google Scholar
     

  • Sato A et al (2008) Cerebellar development transcriptome database (CDT-DB): profiling of spatio-temporal gene expression during the postnatal development of mouse cerebellum. Neural Netw 21(8):1056–1069

    Article 

    Google Scholar
     

  • Johnson KA (2001) The whole brain atlas. Harvard University, Cambridge


    Google Scholar
     

  • Rosen GD et al (2000) The mouse brain [email protected] www.mbl.org. In: International mouse genome conference

  • Ascoli GA, Donohue DE, Halavi M (2007) NeuroMorpho.Org: a central resource for neuronal morphologies. J Neurosci 27(35):9247–9251

    Article 

    Google Scholar
     

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105


    Google Scholar
     

  • Allen-Zhu Z, Li Y (2019) What can ResNet learn efficiently, going beyond kernels? arXiv preprint arXiv:1905.10337

  • Szegedy C et al (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence

  • Szegedy C et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  • Huang G et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  • Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434

  • Szegedy C et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  • Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610

    Article 

    Google Scholar
     

  • Graves A, Jaitly N, Mohamed A-R (2013) Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE workshop on automatic speech recognition and understanding. IEEE

  • Chung J et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

  • Li X et al (2020) Fast confocal microscopy imaging based on deep learning. In: 2020 IEEE international conference on computational photography (ICCP). IEEE

  • Weigert M et al (2017) Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer

  • Ouyang W et al (2018) Deep learning massively accelerates super-resolution localization microscopy. Nat Biotechnol 36(5):460–468

    Article 

    Google Scholar
     

  • Nehme E et al (2018) Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5(4):458–464

    Article 

    Google Scholar
     

  • Wang Z et al (2021) Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning. Nat Methods 18(5):551–556

    Article 

    Google Scholar
     

  • Laine S et al (2019) High-quality self-supervised deep image denoising. arXiv preprint arXiv:1901.10277

  • Krull A et al (2020) Probabilistic noise2void: unsupervised content-aware denoising. Front Comput Sci 2:5

    Article 

    Google Scholar
     

  • Lehtinen J et al (2018) Noise2noise: learning image restoration without clean data. arXiv preprint arXiv:1803.04189

  • Buchholz T-O et al (2019) Cryo-care: content-aware image restoration for cryo-transmission electron microscopy data. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE

  • Batson J, Royer L (2019) Noise2self: Blind denoising by self-supervision. In: International conference on machine learning. PMLR

  • Zhu J-Y et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision

  • Fan J et al (2019) BIRNet: brain image registration using dual-supervised fully convolutional networks. Med Image Anal 54:193–206

    Article 

    Google Scholar
     

  • Cao X et al (2017) Deformable image registration based on similarity-steered CNN regression. In: International conference on medical image computing and computer-assisted intervention. Springer

  • Haberl MG et al (2018) CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods 15(9):677–680

    Article 

    Google Scholar
     

  • Stringer C et al (2020) Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18(1):100–106

    Article 

    Google Scholar
     

  • Januszewski M et al (2018) High-precision automated reconstruction of neurons with flood-filling networks. Nat Methods 15(8):605–610

    Article 

    Google Scholar
     

  • Zhou Z et al (2018) DeepNeuron: an open deep learning toolbox for neuron tracing. Brain Inform 5(2):3

    Article 

    Google Scholar
     

  • Church KW (2017) Word2Vec. Nat Lang Eng 23(1):155–162

    Article 

    Google Scholar
     

  • Vaswani A et al (2017) Attention is all you need. arXiv preprint arXiv:1706.03762

  • Abe T et al (2021) Neuroscience cloud analysis as a service. bioRxiv. https://doi.org/10.1101/2020.06.11.146746

    Article 

    Google Scholar
     

  • Agrawal D, Das S, El Abbadi A (2011) Big data and cloud computing: current state and future opportunities. In: Proceedings of the 14th international conference on extending database technology

  • Berg S et al (2019) Ilastik: interactive machine learning for (bio) image analysis. Nat Methods 16(12):1226–1232

    Article 

    Google Scholar
     

  • Ouyang W et al (2019) ImJoy: an open-source computational platform for the deep learning era. Nat Methods 16(12):1199–1200

    Article 

    Google Scholar
     

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