{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:25:27Z","timestamp":1785403527973,"version":"3.56.0"},"posted":{"date-parts":[[2025,4,18]]},"group-title":"Bioengineering","reference-count":34,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T00:00:00Z","timestamp":1744934400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["1081\/24"],"award-info":[{"award-number":["1081\/24"]}],"id":[{"id":"https:\/\/ror.org\/04sazxf24","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R35GM155365"],"award-info":[{"award-number":["R35GM155365"]}],"id":[{"id":"https:\/\/ror.org\/04q48ey07","id-type":"ROR","asserted-by":"publisher"}]},{"award":["RR200025"],"award-info":[{"award-number":["RR200025"]}],"id":[{"id":"https:\/\/ror.org\/0003xa228","id-type":"ROR","asserted-by":"publisher"}]},{"award":["GRK 2566"],"award-info":[{"award-number":["GRK 2566"]}],"id":[{"id":"https:\/\/ror.org\/018mejw64","id-type":"ROR","asserted-by":"publisher"}]},{"award":["CRC 1177"],"award-info":[{"award-number":["CRC 1177"]}],"id":[{"id":"https:\/\/ror.org\/018mejw64","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,4,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and computation expertise to address the compromise between model performance and its generalization. For example, researchers manually tune parameters to generate training images that are similar to their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Here, we introduce AutoDS and AutoDS3D, two software programs for reconstruction of single-molecule super-resolution microscopy data that are based on Deep-STORM and DeepSTORM3D, that significantly reduce human intervention from the analysis process by automatically extracting the experimental parameters from the imaging raw data. In the 2D case, AutoDS selects the optimal model for the analysis out of a set of pre-trained models, hence, completely removing user supervision from the process. In the 3D case, we improve the computation efficiency of DeepSTORM3D and integrate the lengthy workflow into a graphic user interface that enables image reconstruction with a single click. Ultimately, we demonstrate superior performance of both pipelines compared to Deep-STORM and DeepSTORM3D for single-molecule imaging data of complex biological samples, while significantly reducing the manual labor and computation time.<\/jats:p>","DOI":"10.1101\/2025.04.13.648574","type":"posted-content","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T12:04:58Z","timestamp":1745409898000},"source":"Crossref","is-referenced-by-count":1,"title":["One-click image reconstruction in single-molecule localization microscopy via deep learning"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3852-7628","authenticated-orcid":false,"given":"Alon","family":"Saguy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5126-9742","authenticated-orcid":false,"given":"Dafei","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9977-6500","authenticated-orcid":false,"given":"Kaarjel K.","family":"Narayanasamy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8909-360X","authenticated-orcid":false,"given":"Yuya","family":"Nakatani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0980-1168","authenticated-orcid":false,"given":"Anna-Karin","family":"Gustavsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9821-3578","authenticated-orcid":false,"given":"Mike","family":"Heilemann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8498-5203","authenticated-orcid":false,"given":"Yoav","family":"Shechtman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2025052505081702000_2025.04.13.648574v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth929"},{"key":"2025052505081702000_2025.04.13.648574v1.2","doi-asserted-by":"publisher","DOI":"10.1002\/anie.200802376"},{"key":"2025052505081702000_2025.04.13.648574v1.3","doi-asserted-by":"crossref","unstructured":"Jungmann, R. et al. Single-molecule kinetics and super-resolution microscopy by fluorescence imaging of transient binding on DNA origami. Nano Letters 10, (2010).","DOI":"10.1021\/nl103427w"},{"key":"2025052505081702000_2025.04.13.648574v1.4","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127344"},{"key":"2025052505081702000_2025.04.13.648574v1.5","doi-asserted-by":"publisher","DOI":"10.1529\/biophysj.106.091116"},{"key":"2025052505081702000_2025.04.13.648574v1.6","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.6b00667"},{"key":"2025052505081702000_2025.04.13.648574v1.7","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0900245106"},{"key":"2025052505081702000_2025.04.13.648574v1.8","doi-asserted-by":"crossref","unstructured":"Shechtman, Y. , Sahl, S. J. , Backer, A. S. & Moerner, W. E. Optimal point spread function design for 3D imaging. Physical Review Letters 113, (2014).","DOI":"10.1103\/PhysRevLett.113.133902"},{"key":"2025052505081702000_2025.04.13.648574v1.9","doi-asserted-by":"publisher","DOI":"10.1364\/OPTICA.5.000458"},{"key":"2025052505081702000_2025.04.13.648574v1.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-021-01236-x"},{"key":"2025052505081702000_2025.04.13.648574v1.11","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-0853-5"},{"key":"2025052505081702000_2025.04.13.648574v1.12","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-01966-0"},{"key":"2025052505081702000_2025.04.13.648574v1.13","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad137"},{"key":"2025052505081702000_2025.04.13.648574v1.14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-54609-z"},{"key":"2025052505081702000_2025.04.13.648574v1.15","doi-asserted-by":"publisher","DOI":"10.1364\/OE.388248"},{"key":"2025052505081702000_2025.04.13.648574v1.16","doi-asserted-by":"crossref","unstructured":"Albertazzi, L. & Heilemann, M. When weak is strong: a plea for low affinity binders for optical microscopy. Angewandte Chemie International Edition e202303390 (2023).","DOI":"10.1002\/ange.202303390"},{"key":"2025052505081702000_2025.04.13.648574v1.17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-32626-0"},{"key":"2025052505081702000_2025.04.13.648574v1.18","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpr.2023.100123"},{"key":"2025052505081702000_2025.04.13.648574v1.19","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-018-0216-7"},{"key":"2025052505081702000_2025.04.13.648574v1.20","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-01775-5"},{"key":"2025052505081702000_2025.04.13.648574v1.21","doi-asserted-by":"crossref","unstructured":"Xiao, D. et al. Large-FOV 3D localization microscopy by spatially variant point spread function generation. Science Advances (2024).","DOI":"10.1101\/2023.07.30.551150"},{"key":"2025052505081702000_2025.04.13.648574v1.22","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2017.024"},{"key":"2025052505081702000_2025.04.13.648574v1.23","first-page":"1398","article-title":"Multiscale structural similarity for image quality assessment","volume":"2","year":"2003","journal-title":"in The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003"},{"key":"2025052505081702000_2025.04.13.648574v1.24","unstructured":"Prieto, G. , Chevalier, M. & Guibelalde, E. MS_SSIM Index as a Java plugin for ImageJ. (2014)."},{"key":"2025052505081702000_2025.04.13.648574v1.25","doi-asserted-by":"publisher","DOI":"10.3389\/fnsyn.2021.671288"},{"key":"2025052505081702000_2025.04.13.648574v1.26","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02282-x"},{"key":"2025052505081702000_2025.04.13.648574v1.27","doi-asserted-by":"crossref","first-page":"11379","DOI":"10.1021\/acs.jpcb.4c05141","article-title":"Long-Axial-Range Double-Helix Point Spread Functions for 3D Volumetric Super-Resolution Imaging","volume":"128","year":"2024","journal-title":"J. Phys. Chem. B"},{"key":"2025052505081702000_2025.04.13.648574v1.28","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4661"},{"key":"2025052505081702000_2025.04.13.648574v1.29","doi-asserted-by":"crossref","unstructured":"Krull, A. , Buchholz, T.-O. & Jug, F. Noise2void-learning denoising from single noisy images. in 2129\u20132137 (2019).","DOI":"10.1109\/CVPR.2019.00223"},{"key":"2025052505081702000_2025.04.13.648574v1.30","unstructured":"Goncharova, A. S. , Honigmann, A. , Jug, F. & Krull, A. Improving Blind Spot Denoising for Diffraction-Limited Microscopy Data. in."},{"key":"2025052505081702000_2025.04.13.648574v1.31","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1275-3"},{"key":"2025052505081702000_2025.04.13.648574v1.32","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01018-x"},{"key":"2025052505081702000_2025.04.13.648574v1.33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-44643-z"},{"key":"2025052505081702000_2025.04.13.648574v1.34","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.6966132"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2025.04.13.648574","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T08:30:46Z","timestamp":1768465846000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2025.04.13.648574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,18]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1101\/2025.04.13.648574","relation":{},"subject":[],"published":{"date-parts":[[2025,4,18]]},"subtype":"preprint"}}