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In: Advances in Neural Information Processing Systems; 2015. p. 3573\u20133581."},{"key":"ref10","unstructured":"Santurkar S, Budden D, Matveev A, Berlin H, Saribekyan H, Meirovitch Y, et al. Toward streaming synapse detection with compositional convnets. arXiv preprint arXiv:170207386. 2017."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jneumeth.2013.12.003","article-title":"Evaluation of the effectiveness of Gaussian filtering in distinguishing punctate synaptic signals from background noise during image analysis","volume":"223","author":"S Iwabuchi","year":"2014","journal-title":"Journal of neuroscience methods"},{"issue":"2","key":"ref12","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1023\/A:1008045108935","article-title":"Feature detection with automatic scale selection","volume":"30","author":"T Lindeberg","year":"1998","journal-title":"International journal of computer vision"},{"issue":"10","key":"ref13","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1364\/AO.46.001819","article-title":"Gaussian approximations of fluorescence microscope point-spread function models","volume":"46","author":"B Zhang","year":"2007","journal-title":"Applied optics"},{"key":"ref14","first-page":"283","article-title":"On the diffraction of an object-glass with circular aperture","volume":"5","author":"GB Airy","year":"1835","journal-title":"Transactions of the Cambridge Philosophical Society"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. p. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"14","key":"ref16","doi-asserted-by":"crossref","first-page":"5792","DOI":"10.1523\/JNEUROSCI.4274-14.2015","article-title":"Mapping synapses by conjugate light-electron array tomography","volume":"35","author":"F Collman","year":"2015","journal-title":"Journal of Neuroscience"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"140046","DOI":"10.1038\/sdata.2014.46","article-title":"Synaptic molecular imaging in spared and deprived columns of mouse barrel cortex with array tomography","volume":"1","author":"NC Weiler","year":"2014","journal-title":"Scientific data"},{"issue":"22","key":"ref18","doi-asserted-by":"crossref","first-page":"4057","DOI":"10.1091\/mbc.E15-06-0448","article-title":"Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking","volume":"26","author":"CS Smith","year":"2015","journal-title":"Molecular biology of the cell"},{"issue":"3","key":"ref19","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1083\/jcb.201106158","article-title":"All three components of the neuronal SNARE complex contribute to secretory vesicle docking","volume":"198","author":"Y Wu","year":"2012","journal-title":"J Cell Biol"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Gudla PR, Nakayama K, Pegoraro G, Misteli T. SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ Hybridization (FISH) Signals in High-Throughput Imaging Approaches. In: Cold Spring Harbor symposia on quantitative biology. Cold Spring Harbor Laboratory Press; 2017. p. 033761.","DOI":"10.1101\/sqb.2017.82.033761"},{"issue":"3","key":"ref21","doi-asserted-by":"crossref","first-page":"121","DOI":"10.4251\/wjgo.v9.i3.121","article-title":"Gastric peritoneal carcinomatosis-a retrospective review","volume":"9","author":"HL Tan","year":"2017","journal-title":"World journal of gastrointestinal oncology"},{"key":"ref22","unstructured":"Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in neural information processing systems; 2012. p. 2843\u20132851."},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Shavit N. A Multicore Path to Connectomics-on-Demand. In: Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures. ACM; 2016. p. 211\u2013211.","DOI":"10.1145\/2935764.2935825"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref25","unstructured":"Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:151107122. 2015."},{"key":"ref26","unstructured":"Paszke A, Chaurasia A, Kim S, Culurciello E. Enet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:160602147. 2016."},{"issue":"10","key":"ref27","doi-asserted-by":"crossref","first-page":"e24899","DOI":"10.1371\/journal.pone.0024899","article-title":"Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images","volume":"6","author":"A Kreshuk","year":"2011","journal-title":"PloS one"},{"issue":"10","key":"ref28","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1109\/TMI.2013.2267747","article-title":"Learning context cues for synapse segmentation","volume":"32","author":"C Becker","year":"2013","journal-title":"IEEE transactions on medical imaging"},{"issue":"1","key":"ref29","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jneumeth.2011.11.010","article-title":"Automated quantification of synapses by fluorescence microscopy","volume":"204","author":"P Sch\u00e4tzle","year":"2012","journal-title":"Journal of neuroscience methods"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Herold J, Friedenberger M, Bode M, Rajpoot N, Schubert W, Nattkemper TW. Flexible synapse detection in fluorescence micrographs by modeling human expert grading. In: Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on. IEEE; 2008. p. 1347\u20131350.","DOI":"10.1109\/ISBI.2008.4541254"},{"issue":"4","key":"ref31","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.jbiotec.2010.03.004","article-title":"Automated detection and quantification of fluorescently labeled synapses in murine brain tissue sections for high throughput applications","volume":"149","author":"J Herold","year":"2010","journal-title":"Journal of biotechnology"},{"issue":"4","key":"ref32","doi-asserted-by":"crossref","first-page":"e1005493","DOI":"10.1371\/journal.pcbi.1005493","article-title":"Probabilistic fluorescence-based synapse detection","volume":"13","author":"AK Simhal","year":"2017","journal-title":"PLoS computational biology"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science; 1985.","DOI":"10.21236\/ADA164453"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In: The IEEE International Conference on Computer Vision (ICCV); 2017.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 3D Vision (3DV), 2016 Fourth International Conference on. IEEE; 2016. p. 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref36","unstructured":"PyTorch Tensors and Dynamic neural networks in Python with strong GPU acceleration; 2017. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/pytorch.org\" xlink:type=\"simple\">http:\/\/pytorch.org<\/ext-link>."},{"key":"ref37","unstructured":"Brown M, Szeliski R, Winder S. Multi-image matching using multi-scale oriented patches. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. vol. 1. IEEE; 2005. p. 510\u2013517."},{"issue":"3","key":"ref38","doi-asserted-by":"crossref","first-page":"e91744","DOI":"10.1371\/journal.pone.0091744","article-title":"High content image analysis identifies novel regulators of synaptogenesis in a high-throughput RNAi screen of primary neurons","volume":"9","author":"TJ Nieland","year":"2014","journal-title":"PloS one"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3389\/fnana.2015.00060","article-title":"FIB\/SEM technology and high-throughput 3D reconstruction of dendritic spines and synapses in GFP-labeled adult-generated neurons","volume":"9","author":"C Bosch","year":"2015","journal-title":"Frontiers in neuroanatomy"},{"issue":"3","key":"ref40","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1038\/nmeth.2843","article-title":"Precisely and accurately localizing single emitters in fluorescence microscopy","volume":"11","author":"H Deschout","year":"2014","journal-title":"Nature methods"},{"issue":"1","key":"ref41","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1111\/j.1365-2818.2012.03675.x","article-title":"3-D PSF fitting for fluorescence microscopy: implementation and localization application","volume":"249","author":"H Kirshner","year":"2013","journal-title":"Journal of microscopy"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1007012","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T00:00:00Z","timestamp":1558569600000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/dx.plos.org\/10.1371\/journal.pcbi.1007012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T23:25:57Z","timestamp":1663457157000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1007012"}},"subtitle":[],"editor":[{"given":"Joshua","family":"Vogelstein","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,5,13]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5,13]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1007012","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1007012","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,13]]}}}