Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis
Open accessPublished version of a preprint
Abstract
Activity recognition in live-cell imaging is labor-intensive and requires significant human effort. Existing automated analysis tools are largely limited in versatility. We present the Intelligent Vesicle Exocytosis Analysis (IVEA) platform, an ImageJ plugin for automated, reliable analysis of fluorescence-labeled vesicle fusion events and other burst-like activity. IVEA includes three specialized modules for detecting: (1) synaptic transmission in neurons, (2) single-vesicle exocytosis in any cell type, and (3) nano-sensor-detected exocytosis. Each module uses distinct techniques, including deep learning, allowing the detection of rare events often missed by humans at a speed estimated to be approximately 60 times faster than manual analysis. IVEA’s versatility can be expanded by refining or training new models via an integrated interface. With its impressive speed and remarkable accuracy, IVEA represents a seminal advancement in exocytosis image analysis and other burst-like fluorescence fluctuations applicable to a wide range of microscope types and fluorescent dyes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works
Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins
Miesenböck, G., Angelis, D. A. D., & Rothman, J. E. (1998) · Nature · doi:10.1038/28190 · Cited by 2,569 · see citing works
Cellpose 2.0: how to train your own model
Pachitariu, M., & Stringer, C. (2022) · Nature Methods · doi:10.1038/s41592-022-01663-4 · Cited by 1,381 · see citing works
Multivariate LSTM-FCNs for time series classification
Karim, F., Majumdar, S., Darabi, H., & Harford, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.014 · Cited by 1,138 · see citing works
Hohmeier, H. E., Mulder, H., Chen, G., Henkel-Rieger, R., Prentki, M., & Newgard, C. B. (2000) · Diabetes · doi:10.2337/diabetes.49.3.424 · Cited by 922 · see citing works
Weighted boxes fusion: Ensembling boxes from different object detection models
Solovyev, R., Wang, W., & Gabruseva, T. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104117 · Cited by 543 · see citing works
TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A
Brown, A., Wilkins, O. G., Keuss, M. J., Kargbo-Hill, S. E., Zanovello, M., Lee, W. C., Bampton, A., Lee, F. C. Y., Masino, L., Qi, Y. A., et al. (2022) · Nature · doi:10.1038/s41586-022-04436-3 · Cited by 526 · see citing works
The kinetics of synaptic vesicle recycling measured at single presynaptic boutons
Ryan, T. A., REUTER, H., Wendland, B., Schweizer, F. E., Tsien, R. W., & Smith, S. J. (1993) · Neuron · doi:10.1016/0896-6273(93)90081-2 · Cited by 517 · see citing works
Transport, docking and exocytosis of single secretory granules in live chromaffin cells
Steyer, J. A., Horstmann, H., & Almers, W. (1997) · Nature · doi:10.1038/41329 · Cited by 443 · see citing works
Synapsin dispersion and reclustering during synaptic activity
Chi, P., Greengard, P., & Ryan, T. A. (2001) · Nature Neuroscience · doi:10.1038/nn756 · Cited by 362 · see citing works
Quantitative mapping and minimization of super-resolution optical imaging artifacts
Culley, S., Albrecht, D., Jacobs, C., Pereira, P. M., Leterrier, C., Mercer, J., & Henriques, R. (2018) · Nature Methods · doi:10.1038/nmeth.4605 · Cited by 357 · see citing works
Quantifying exosome secretion from single cells reveals a modulatory role for GPCR signaling
Verweij, F. J., Bebelman, M. P., Jiménez, C. R., García-Vallejo, J. J., Janssen, H., Neefjes, J., Knol, J. C., Haas, R. D. G.-D., Piersma, S. R., Baglio, S. R., et al. (2018) · The Journal of Cell Biology · doi:10.1083/jcb.201703206 · Cited by 356 · see citing works
Calcium accelerates endocytosis of vSNAREs at hippocampal synapses
Sankaranarayanan, S., & Ryan, T. A. (2001) · Nature Neuroscience · doi:10.1038/83949 · Cited by 295 · see citing works
Carbon Nanomaterial Fluorescent Probes and Their Biological Applications
Krasley, A. T., Li, E., Galeana, J. M., Bulumulla, C., Beyene, A. G., & Demirer, G. S. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00581 · Cited by 250 · see citing works
Barg, S., Olofsson, C. S., Schriever-Abeln, J., Wendt, A., Gebre-Medhin, S., Renström, E., & Rorsman, P. (2002) · Neuron · doi:10.1016/s0896-6273(02)00563-9 · Cited by 210 · see citing works
Self-citationFernández-Alfonso, T., Kwan, R., & Ryan, T. A. (2006) · Neuron · doi:10.1016/j.neuron.2006.06.008 · Cited by 202 · see citing works
Aggarwal, A., Liu, R., Chen, Y., Ralowicz, A. J., Bergerson, S. J., Tomáška, F., Mohar, B., Hanson, T., Hasseman, J. P., Reep, D., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-01863-6 · Cited by 192 · see citing works
A readily retrievable pool of synaptic vesicles
Hua, Y., Sinha, R., Thiel, C. S., Schmidt, R., Hüve, J., Martens, H., Hell, S. W., Egner, A., & Klingauf, J. (2011) · Nature Neuroscience · doi:10.1038/nn.2838 · Cited by 181 · see citing works
Fluorescence Microscopy and Fluorescent Probes
Slavı́k, J. (1996) · Springer eBooks · doi:10.1007/978-1-4899-1866-6 · Cited by 160 · see citing works
Calcium regulates exocytosis at the level of single vesicles
Becherer, U., Moser, T., Stühmer, W., & Oheim, M. (2003) · Nature Neuroscience · doi:10.1038/nn1087 · Cited by 150 · see citing works
Self-citationAshery, U., Betz, A., Xu, T., Brose, N., & Rettig, J. (1999) · European Journal of Cell Biology · doi:10.1016/s0171-9335(99)80017-x · Cited by 99 · see citing works
Marshall, M. R., Pattu, V., Halimani, M., Maier‐Peuschel, M., Müller, M., Becherer, U., Hong, W., Hoth, M., Tschernig, T., Bryceson, Y. T., et al. (2015) · The Journal of Cell Biology · doi:10.1083/jcb.201411093 · Cited by 92 · see citing works
Self-citationElizarova, S., Chouaib, A. A., Shaib, A., Hill, B., Mann, F. A., Brose, N., Kruss, S., & Daniel, J. A. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2202842119 · Cited by 83 · see citing works
Self-citationZhu, D., Koo, E., Kwan, E., Kang, Y., Park, S., Xie, H., Sugita, S., & Gaisano, H. Y. (2013) · Diabetologia · doi:10.1007/s00125-012-2757-0 · Cited by 81 · see citing works
Live-cell imaging powered by computation
Shroff, H., Testa, I., Jug, F., & Manley, S. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00702-6 · Cited by 75 · see citing works
Chang, H., Schirra, C., Ninov, M., Hahn, U., Ravichandran, K., Krause, E., Becherer, U., Bálint, Š., Harkiolaki, M., Urlaub, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28596-y · Cited by 69 · see citing works
Self-citationRecent Insights into Beta-cell Exocytosis in Type 2 Diabetes
Thurmond, D. C., & Gaisano, H. Y. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.12.012 · Cited by 53 · see citing works
Zhao, Y., Fang, Q., Herbst, A. D., Berberian, K., Almers, W., & Lindau, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1306699110 · Cited by 48 · see citing works
Visualizing synaptic dopamine efflux with a 2D composite nanofilm
Bulumulla, C., Krasley, A. T., Cristofori‐Armstrong, B., Valinsky, W. C., Walpita, D., Ackerman, D., Clapham, D. E., & Beyene, A. G. (2022) · eLife · doi:10.7554/elife.78773 · Cited by 44 · see citing works
Becherer, U., Pasche, M., Nofal, S., Hof, D., Matti, U., & Rettig, J. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000505 · Cited by 44 · see citing works
Self-citationImaging of Monoamine Neurotransmitters with Fluorescent Nanoscale Sensors
Dinarvand, M., Elizarova, S., Daniel, J. A., & Kruss, S. (2020) · ChemPlusChem · doi:10.1002/cplu.202000248 · Cited by 43 · see citing works
Self-citationOptical detection of synaptic vesicle exocytosis and endocytosis
Murthy, V. N. (1999) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(99)80046-4 · Cited by 40 · see citing works
Genetics and pathophysiology of haemophagocytic lymphohistiocytosis
Meeths, M., & Bryceson, Y. T. (2021) · Acta Paediatrica · doi:10.1111/apa.16013 · Cited by 35 · see citing works
Synaptotagmin isoforms confer distinct activation kinetics and dynamics to chromaffin cell granules
Rao, T. C., Santana‐Rodriguez, Z., Bradberry, M. M., Ranski, A. H., Dahl, P. J., Schmidtke, M. W., Jenkins, P. M., Axelrod, D., Chapman, E. R., Giovannucci, D. R., et al. (2017) · The Journal of General Physiology · doi:10.1085/jgp.201711757 · Cited by 33 · see citing works
Some Applications of Monotone Operators in Markov Processes
MacQueen, J. B., & Redheffer, R. M. (1965) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177699900 · Cited by 31 · see citing works
Mouchacca, P., Schmitt-Verhulst, A.-M., & Boyer, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067239 · Cited by 29 · see citing works
Spatio-Temporal Analysis of Constitutive Exocytosis in Epithelial Cells
Sebastián, R., Díaz, M. E., Ayala, G., Letinić, K., Moncho-Bogani, J., & Toomre, D. K. (2006) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2006.11 · Cited by 28 · see citing works
Fluorescent-Based Neurotransmitter Sensors: Present and Future Perspectives
Govindaraju, R., Govindaraju, S., Yun, K., & Kim, J. (2023) · Biosensors · doi:10.3390/bios13121008 · Cited by 27 · see citing works
Deciphering Dead-End Docking of Large Dense Core Vesicles in Bovine Chromaffin Cells
Hugo, S., Dembla, E., Halimani, M., Matti, U., Rettig, J., & Becherer, U. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1589-13.2013 · Cited by 24 · see citing works
Self-citationShaib, A., Staudt, A., Harb, A., Klose, M., Shaaban, A., Schirra, C., Mohrmann, R., Rettig, J., & Becherer, U. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00304 · Cited by 22 · see citing works
Self-citationPrediction of corneal back surface power – Deep learning algorithm versus multivariate regression
Langenbucher, A., Szentmáry, N., Cayless, A., Weisensee, J., Wendelstein, J. A., & Hoffmann, P. (2021) · Ophthalmic and Physiological Optics · doi:10.1111/opo.12909 · Cited by 22 · see citing works
Filali, L., Puisségur, M.-P., Cortacero, K., Cussat‐Blanc, S., Khazen, R., Acker, N. V., Frénois, F.-X., Abreu, A., Lamant, L., Meyer, N., et al. (2022) · Science Advances · doi:10.1126/sciadv.abk3234 · Cited by 21 · see citing works
Self-citationRegulated exocytosis in chromaffin cells and cytotoxic T lymphocytes: How similar are they?
Becherer, U., Medart, M. R., Schirra, C., Krause, E., Stevens, D. R., & Rettig, J. (2012) · Cell Calcium · doi:10.1016/j.ceca.2012.04.002 · Cited by 19 · see citing works
Self-citationKu, T., Kao, L., Lin, C., & Tsai, Y. (2009) · Microscopy Research and Technique · doi:10.1002/jemt.20711 · Cited by 19 · see citing works
Genetic disorders of neurotransmitter release machinery
Uzay, B., & Kavalali, E. T. (2023) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2023.1148957 · Cited by 19 · see citing works
Lytic granule exocytosis at immune synapses: lessons from neuronal synapses
Chang, H., Schirra, C., Pattu, V., Krause, E., & Becherer, U. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1177670 · Cited by 19 · see citing works
Self-citationBost, A., Shaib, A., Schwarz, Y. N., Niemeyer, B. A., & Becherer, U. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.01.006 · Cited by 18 · see citing works
Self-citationChitirala, P., Chang, H., Martzloff, P., Harenberg, C., Ravichandran, K., Abdulreda, M. H., Berggren, P., Krause, E., Schirra, C., Leinders‐Zufall, T., et al. (2020) · eLife · doi:10.7554/elife.58065 · Cited by 18 · see citing works
Self-citationLocal PI(4,5)P2 signaling inhibits fusion pore expansion during exocytosis
Omar‐Hmeadi, M., Guček, A., & Barg, S. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112036 · Cited by 18 · see citing works
Self-citationDocking of LDCVs Is Modulated by Lower Intracellular [Ca2+] than Priming
Pasche, M., Matti, U., Hof, D., Rettig, J., & Becherer, U. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036416 · Cited by 17 · see citing works
Self-citationEstl, M., Blatt, P., Li, X., Becherer, U., Chang, H., Rettig, J., & Pattu, V. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072475 · Cited by 16 · see citing works
Self-citationSynActJ: Easy-to-Use Automated Analysis of Synaptic Activity
Schmied, C., Soykan, T., Bolz, S., Haucke, V., & Lehmann, M. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.777837 · Cited by 16 · see citing works
A solution for highly efficient electroporation of primary cytotoxic T lymphocytes
Alawar, N., Schirra, C., Hohmann, M., & Becherer, U. (2024) · BMC Biotechnology · doi:10.1186/s12896-024-00839-4 · Cited by 14 · see citing works
Self-citationCalcium dysregulation in ventricular myocytes from mice expressing constitutively active Rac1
Oberhofer, M., Tian, X. Y., Ruppenthal, S., Wegener, S., Reil, J., Körbel, C., Hammer, K. P., Menger, M. D., Neuberger, H., Kaestner, L., et al. (2013) · Cell Calcium · doi:10.1016/j.ceca.2013.03.006 · Cited by 13 · see citing works
Self-citationAutomated Detection and Analysis of Exocytosis
Urbina, F., & Gupton, S. L. (2021) · Journal of Visualized Experiments · doi:10.3791/62400 · Cited by 10 · see citing works
ExoJ – a Fiji/ImageJ2 plugin for automated spatiotemporal detection and analysis of exocytosis
Liu, J., Verweij, F. J., van Niel, G., Galli, T., Danglot, L., & Bun, P. (2024) · Journal of Cell Science · doi:10.1242/jcs.261938 · Cited by 8 · see citing works
Matz, M., Schumacher, K., Hatlapatka, K., Lorenz, D. A., Baumann, K., & Rustenbeck, I. (2013) · Microscopy and Microanalysis · doi:10.1017/s1431927613013767 · Cited by 8 · see citing works
Large scale, unbiased analysis of elementary calcium signaling events in cardiac myocytes
Tian, X. Y., Schröder, L., Schwarz, Y. N., Flockerzi, A., Kaestner, L., Zeug, A., Bruns, D., & Lipp, P. (2019) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2019.08.004 · Cited by 8 · see citing works
Self-citationStaudt, A., Ratai, O., Bouzouina, A., Fecher‐Trost, C., Shaaban, A., Bzeih, H., Horn, A., Shaib, A., Klose, M., Flockerzi, V., et al. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.674243 · Cited by 6 · see citing works
Self-citationpHusion – a robust and versatile toolset for automated detection and analysis of exocytosis
O’Shaughnessy, E. C., Lam, M., Ryken, S. E., Wiesner, T., Lukasik, K., Zuchero, J. B., Leterrier, C., Adalsteinsson, D., & Gupton, S. L. (2024) · Journal of Cell Science · doi:10.1242/jcs.261828 · Cited by 6 · see citing works
Methods of measuring presynaptic function with fluorescence probes
Jang, Y., Kim, S. R., & Lee, S. H. (2021) · Han-guk hyeonmigyeong hakoeji/Applied microscopy · doi:10.1186/s42649-021-00051-0 · Cited by 5 · see citing works
Apparent calcium spark properties and fast-scanning 2D confocal imaging modalities
Tian, Q., & Lipp, P. (2020) · Cell Calcium · doi:10.1016/j.ceca.2020.102303 · Cited by 2 · see citing works
Self-citationHighly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis
Chouaib, A. A., Chang, D. H.-F., Mohmed Khamis Ibrahim, O., Alawar, N., Echeverry, S., Demeersseman, L., Elizarova, S., Daniel, J. A., Tian, Q., Lipp, P., et al. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15498138 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Engineering Bacterial Extracellular Vesicles as Nanoweapons to Fight against Bacterial Infections
Shi, Y., Li, Y., Liu, Z., Kong, X., Hu, X., Liu, X., Zhang, C., & Hu, H. (2026) · Research · doi:10.34133/research.1135 · Cited by 10 · see citing works
Scalable bioelectronic production of extracellular vesicles for cardiac tissue regeneration
Meng, L., Zhang, J., Li, P., Li, W., Shi, J., Cheng, Z., Ostroff, E., Yang, C., Yue, J., Promiński, A., et al. (2025) · Device · doi:10.1016/j.device.2025.100968 · Cited by 3 · see citing works
Research progress of technology for exocytosis detection in the nervous system
Zhang, F.-L., Xu, C., Zhao, X., Du, S.-Y., & Tang, J. (2025) · The Analyst · doi:10.1039/d5an00934k · Cited by 1 · see citing works
He, S., Li, S., Dai, C., Wang, Y., Liu, J., Sun, R., Feng, G., & Wang, J. (2025) · Applied Optics · doi:10.1364/ao.578957
Harmanec, A., Dagg, A. D., Kamenický, J., Kerepecký, T., Makieieva, Y., Pereira, C., Bright, N. A., Menon, D., Gershlick, D. C., Vaškovicová, N., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.29.715120
Khamis, O. M., Shkeir, M., Schirra, C., Tu, S.-M., Cassioli, C., Chouaib, A. A., Lecomte, E., Schoenecker, L., Dustin, M. L., Chang, H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.12.731857
Self-citationArtificial Intelligence for Discovery in Life Sciences
Chanda, S., Rizzoli, S. O., & Shaib, A. (2026) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.6c00230
Self-citationLi, Y., Zhang, M., Wang, Y., Sang, X., 蔡文琛, Shi, Y., & Hu, H. (2026) · Extracellular Vesicles and Circulating Nucleic Acids · doi:10.20517/evcna.2026.59
Kramer, E., Betancur, L. I., Meek, S., Tosi, S., Manzo, C., Oliva, B., & Gallego, O. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag589
ViTAMIn‐O: Democratizing Computer Vision‐Based Machine Learning for Stem Cell Research
Hamurcu, F., Breunig, M., Varga, Á., Bosch, B., Lindenmayer, J., Kanakapaddy, A. T., Achberger, K., Pashkovskaia, N., Kleger, A., Liebau, S., et al. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70547
Huang, Y., Xie, R., Ma, J., & Yu, L. (2026) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-026-01021-8
Advancing Exosome Research through Artificial Intelligence: Advancements and Applications
Chadha, S. (2026) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1016/j.jpet.2026.105047
- Authors
- Chouaib, A. A., Chang, H.-F., Khamis, O. M., Alawar, N., Echeverry, S., Demeersseman, L., Elizarova, S., Daniel, J. A., Tian, Q., Lipp, P., Fornasiero, E. F., Valitutti, S., Barg, S., Pape, C., Shaib, A. H., & Becherer, U.
- Affiliations
- Saarland University (DE)
- Uppsala University (SE)
- Inserm (FR)
- Max Planck Institute for Multidisciplinary Sciences (DE)
- University of Trieste (IT)
- Multiscale Bioimaging (DE)
- Universitätsmedizin Göttingen (DE)
- Corresponding author
- Shaib, Ali H.
- Becherer, Ute
- ORCID
- Venue
- Nature Communications
- Published
- 2025-07-12
- Volume
- 16
- Issue
- 1
- Pages
- 6450-6450
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12255801
- OpenAlex
- W4412362182
- Cited by
- 11
- References
- 67
- Citations by year
- 2026: 8 · 2025: 3
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Diva portal (Dalarna University Library) (submittedVersion, other-oa)
- HAL (submittedVersion)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (SFB894)
- European Commission (ERC-2021-SyG_951329)
- University of Saarland
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular transport and secretion
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Keywords
- vesicle exocytosis, synaptic transmission, automated image analysis, ImageJ plugin, deep learning, live-cell imaging
- MeSH terms
- Deep Learning, Animals, Exocytosis, Fluorescent Dyes, Humans, Image Processing, Computer-Assisted, Microscopy, Fluorescence, Synaptic Transmission, Neurons, Rats
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
