Xu, C. S. et al. An open-access volume electron microscopy atlas of whole cells and tissues. Nature https://doi.org/10.1038/s41586-021-03992-4 (2021).
Valm, A. M. et al. Applying systems-level spectral imaging and analysis to reveal the organelle interactome. Nature 546, 162–167 (2017).
Turaga, S. C. et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511–538 (2010).
Ciresan, D., Giusti, A., Gambardella, L. M. & Schmidhuber, J. Deep neural networks segment neuronal membranes in electron microscopy images. In Proc. 25th International Conference on Neural Information Processing Systems (eds Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q.) 2843–2851 (Curran Associates, 2012).
Januszewski, M. et al. High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605–610 (2018).
Funke, J. et al. Large scale image segmentation with structured loss based deep learning for connectome reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1669–1680 (2019).
Kreshuk, A. et al. Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PLoS ONE 6, e24899 (2011).
Becker, C., Ali, K., Knott, G. & Fua, P. Learning context cues for synapse segmentation in EM volumes. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2012 (eds Ayache, N., Delingette, H., Golland, P. & Mori, K.) 585–592 (Springer, 2012).
Dorkenwald, S. et al. Automated synaptic connectivity inference for volume electron microscopy. Nat. Methods 14, 435–442 (2017).
Heinrich, L., Funke, J., Pape, C., Nunez-Iglesias, J. & Saalfeld, S. Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2018 (eds Frangi, A. et al.) 317–325 (Springer, 2018).
Buhmann, J. et al. Automatic detection of synaptic partners in a whole-brain Drosophila EM dataset. Nat. Methods 18, 771–774 (2021)
Lucchi, A., Li, Y., Smith, K. & Fua, P. Structured image segmentation using kernelized features. In Computer Vision—ECCV 2012 (eds Fitzgibbon, A. et al.) 400–413 (Springer, 2012).
Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).
Lucchi, A. et al. Learning structured models for segmentation of 2-D and 3-D imagery. IEEE Trans. Med. Imaging 34, 1096–1110 (2015).
Márquez Neila, P. et al. A fast method for the segmentation of synaptic junctions and mitochondria in serial electron microscopic images of the brain. Neuroinformatics 14, 235–250 (2016).
Oztel, I., Yolcu, G., Ersoy, I., White, T. & Bunyak, F. Mitochondria segmentation in electron microscopy volumes using deep convolutional neural network. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 1195–1200 (IEEE, 2017).
Cetina, K., Buenaposada, J. M. & Baumela, L. Multi-class segmentation of neuronal structures in electron microscopy images. BMC Bioinformatics 19, 298 (2018).
Casser, V., Kang, K., Pfister, H. & Haehn, D. Fast mitochondria detection for connectomics. In Proceedings of Machine Learning Research (PMRL). (Eds Arbel, T. et al.) 121, 111–120 (2020).
Wei, D. et al. MitoEM dataset: large-scale 3D mitochondria instance segmentation from EM images. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2020 (eds Martel, A. L. et al.) 66–76 (Springer, 2020).
Perez, A. J. et al. A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front. Neuroanat. 8, 126 (2014).
Tek, F. B., Boray Tek, F., Kroeger, T., Mikula, S. & Hamprecht, F. A. Automated cell nucleus detection for large-volume electron microscopy of neural tissue. In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) 69–72 (IEEE, 2014).
Narasimha, R., Ouyang, H., Gray, A., McLaughlin, S. W. & Subramaniam, S. Automatic joint classification and segmentation of whole cell 3D images. Pattern Recognit. 42, 1067–1079 (2009).
Rigamonti, R., Lepetit, V. & Fua, P. Beyond KernelBoost. https://infoscience.epfl.ch/record/200378/files/rigamonti_tr14a_1.pdf (2014).
Karabağ, C. et al. Segmentation and modelling of the nuclear envelope of HeLa cells imaged with serial block face scanning electron microscopy. J. Imaging 5, 75 (2019).
Spiers, H. et al. Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations. Traffic 22, 240–253 (2021).
Žerovnik Mekuč, M. et al. Automatic segmentation of mitochondria and endolysosomes in volumetric electron microscopy data. Comput. Biol. Med. 119, 103693 (2020).
Liu, J. et al. Automatic reconstruction of mitochondria and endoplasmic reticulum in electron microscopy volumes by deep learning. Front. Neurosci. 14, 599 (2020).
Eckstein, N., Buhmann, J., Cook, M. & Funke, J. Microtubule tracking in electron microscopy volumes. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2020 (eds Martel, A. L. et al.) 99–108 (Springer, 2020).
Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015 (eds Navab, N., Hornegger, J., Wells, W. & Frangi, A.) 234–241 (Springer, 2015).
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016 (eds Ourselin, S. et al.) 424–432 (Springer, 2016).
Funke, J., Wu, J., Barnes, C. Waterz—simple watershed and agglomeration library for affinity graphs. GitHub https://github.com/funkey/waterz/tree/7c530ac (2020).
Zlateski, A. & Seung, H. S. Image segmentation by size-dependent single linkage clustering of a watershed basin graph. Preprint at https://arxiv.org/abs/1505.00249 (2015).
Barlan, K. & Gelfand, V. I. Microtubule-based transport and the distribution, tethering, and organization of organelles. Cold Spring Harb. Perspect. Biol. 9, a025817 (2017).
Goyal, U. & Blackstone, C. Untangling the web: mechanisms underlying ER network formation. Biochim. Biophys. Acta Mol. Cell Res. 1833, 2492–2498 (2013).
Blackstone, C. Cellular pathways of hereditary spastic paraplegia. Annu. Rev. Neurosci. 35, 25–47 (2012).
Descoteaux, M., Audette, M., Chinzei, K. & Siddiqi, K. Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery. Comput. Aided Surg. 11, 247–255 (2006).
Nixon-Abell, J. et al. Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER. Science 354, aaf3928 (2016).
Terasaki, M. et al. Stacked endoplasmic reticulum sheets are connected by helicoidal membrane motifs. Cell 154, 285–296 (2013).
Coulter, M. E. et al. The ESCRT-III protein CHMP1A mediates secretion of sonic hedgehog on a distinctive subtype of extracellular vesicles. Cell Rep. 24, 973–986 (2018).
Hoffman, D. P. et al. Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells. Science 367, eaaz5357 (2020).
Xu, C. S. et al. Enhanced FIB-SEM systems for large-volume 3D imaging. Elife 6, e25916 (2017).
Saalfeld, S., Cardona, A., Hartenstein, V. & Tomančák, P. As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets. Bioinformatics 26, i57–i63 (2010).
Saalfeld, S., Pisarev, I. Hanslovsky, P., Bogovic, J.A., Champion, A., Rueden, C., Kirkham, J.A. N5—a scalable Java API for hierarchies of chunked n-dimensional tensors and structured meta-data. GitHub https://github.com/saalfeldlab/n5/tree/n5-2.5.1 (2021)
Pavelka, M. & Roth, J. Functional Ultrastructure: Atlas of Tissue Biology and Pathology (Springer, 2015).
Saalfeld, S., Funke, J., Pietzsch T., Nunez-Iglesias, J., Hanslovsky, P., Bogovic, J., Wolny, A., Melnikov, E. BigCAT. GitHub https://github.com/saalfeldlab/bigcat/tree/0.0.3-beta-1 (2018).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Rueden, C., Schindelin, J., Hiner, M., Arganda-Carreras, I., Skeletonize3D. GitHub https://github.com/fiji/Skeletonize3D/tree/Skeletonize3D_-2.1.1 (2017).
Lee, T. C., Kashyap, R. L. & Chu, C. N. Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP Graph. Models Image Process. 56, 462–478 (1994).
Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010).
Maitin-Shepard, J. et al. Neuroglancer. GitHub https://github.com/google/neuroglancer/tree/v2.22 (2021).
Abramov, D. et al. React. GitHub https://github.com/facebook/react/tree/v17.0.2 (2021).
Perlman, E. Visualizing and interacting with large imaging data. Microsc. Microanal. 25, 1374–1375 (2019).
Hubbard, P. M. et al. Accelerated EM connectome reconstruction using 3D visualization and segmentation graphs. Preprint at https://doi.org/10.1101/2020.01.17.909572 (2020).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Pietzsch, T., Saalfeld, S., Preibisch, S. & Tomancak, P. BigDataViewer: visualization and processing for large image data sets. Nat. Methods 12, 481–483 (2015).
Bogovic, J.A., Saalfeld, S., Hulbert, C., Pisarev, I., Rueden, C., Moon, HK., Preibisch, S. N5-IJ. GitHub https://github.com/saalfeldlab/n5-ij/tree/n5-ij-3.0.0 (2021).
Amazon Web Services. What is the AWS Command Line Interface? https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-welcome.html (2021).
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