{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T04:14:28Z","timestamp":1772079268972,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T00:00:00Z","timestamp":1695772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2022R1A4A2000748"],"award-info":[{"award-number":["NRF-2022R1A4A2000748"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface plasmon resonance microscopy (SPRM) combines the principles of traditional microscopy with the versatility of surface plasmons to develop label-free imaging methods. This paper describes a proof-of-principles approach based on deep learning that utilized the Y-Net convolutional neural network model to improve the detection and analysis methodology of SPRM. A machine-learning based image analysis technique was used to provide a method for the one-shot analysis of SPRM images to estimate scattering parameters such as the scatterer location. The method was assessed by applying the approach to SPRM images and reconstructing an image from the network output for comparison with the original image. The results showed that deep learning can localize scatterers and predict other variables of scattering objects with high accuracy in a noisy environment. The results also confirmed that with a larger field of view, deep learning can be used to improve traditional SPRM such that it localizes and produces scatterer characteristics in one shot, considerably increasing the detection capabilities of SPRM.<\/jats:p>","DOI":"10.3390\/s23198100","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T03:49:14Z","timestamp":1695786554000},"page":"8100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering"],"prefix":"10.3390","volume":"23","author":[{"given":"Jongha","family":"Lee","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gwiyeong","family":"Moon","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sukhyeon","family":"Ka","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kar-Ann","family":"Toh","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1960-0527","authenticated-orcid":false,"given":"Donghyun","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1146\/annurev-anchem-061020-014723","article-title":"Label-Free Super-Resolution Imaging Techniques","volume":"15","author":"Leighton","year":"2022","journal-title":"Annu. Rev. Anal. Chem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1364\/OL.43.000959","article-title":"Surface Plasmon Microscopy by Spatial Light Switching for Label-free Imaging with Enhanced Resolution","volume":"43","author":"Son","year":"2018","journal-title":"Opt. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3426","DOI":"10.1039\/c3cs60479a","article-title":"Nanomaterials Enhanced Surface Plasmon Resonance for Biological and Chemical Sensing Applications","volume":"43","author":"Zeng","year":"2014","journal-title":"Chem. Soc. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1038\/s41592-019-0664-8","article-title":"It\u2019s Free Imaging\u2014Label-Free, That Is","volume":"16","author":"Marx","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1126\/science.1165758","article-title":"Label-Free Biomedical Imaging with High Sensitivity by Stimulated Raman Scattering Microscopy","volume":"322","author":"Freudiger","year":"2008","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1021\/ac200157p","article-title":"Surface Plasmon Resonance Phase Imaging Measurements of Patterned Monolayers and DNA Adsorption onto Microarrays","volume":"83","author":"Halpern","year":"2011","journal-title":"Anal. Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.1021\/ac062284x","article-title":"Surface Plasmon Resonance Imaging Using a High Numerical Aperture Microscope Objective","volume":"79","author":"Huang","year":"2007","journal-title":"Anal. Chem."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9650","DOI":"10.1021\/acs.analchem.8b02800","article-title":"Point Spread Function of Objective-Based Surface Plasmon Resonance Microscopy","volume":"90","author":"Jiang","year":"2018","journal-title":"Anal. Chem."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Son, T., and Kim, D. (2015, January 11). Theoretical Approach to Surface Plasmon Scattering Microscopy for Single Nanoparticle Detection in Near Infrared Region. Proceedings of the Plasmonics in Biology and Medicine XII. Vol. 9340. (SPIE 2015), San Francisco, CA, USA.","DOI":"10.1117\/12.2078243"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10206","DOI":"10.1021\/acs.analchem.6b02878","article-title":"Detection and Quantification of Single Engineered Nanoparticles in Complex Samples Using Template Matching in Wide-Field Surface Plasmon Microscopy","volume":"88","author":"Nizamov","year":"2016","journal-title":"Anal. Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/S0006-3495(99)77219-X","article-title":"Imaging of Cell\/Substrate Contacts of Living Cells with Surface Plasmon Resonance Microscopy","volume":"76","author":"Giebel","year":"1999","journal-title":"Biophys. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1021\/nn405868e","article-title":"Single-Nanoparticle Near-Infrared Surface Plasmon Resonance Microscopy for Real-Time Measurements of DNA Hybridization Adsorption","volume":"8","author":"Halpern","year":"2014","journal-title":"ACS Nano"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1002\/anie.201908806","article-title":"Surface Plasmon Resonance Microscopy: From Single-Molecule Sensing to Single-Cell Imaging","volume":"59","author":"Zhou","year":"2020","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.optcom.2017.10.001","article-title":"Label-Free Quantification of Cell-to-Substrate Separation by Surface Plasmon Resonance Microscopy","volume":"422","author":"Son","year":"2018","journal-title":"Opt. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3873","DOI":"10.1021\/acs.analchem.7b00251","article-title":"Ionic Referencing in Surface Plasmon Microscopy: Visualization of the Difference in Surface Properties of Patterned Monomolecular Layers","volume":"89","author":"Nizamov","year":"2017","journal-title":"Anal. Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1364\/JOSAB.27.001252","article-title":"Subwavelength Grating-Based Nanoplasmonic Modulation for Surface Plasmon Resonance Imaging with Enhanced Resolution","volume":"27","author":"Kim","year":"2010","journal-title":"J. Opt. Soc. Am. B"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"16028","DOI":"10.1073\/pnas.1005264107","article-title":"Label-Free Imaging, Detection, and Mass Measurement of Single Viruses by Surface Plasmon Resonance","volume":"107","author":"Wang","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13373","DOI":"10.1021\/la301712h","article-title":"Mapping Single-Cell\u2013Substrate Interactions by Surface Plasmon Resonance Microscopy","volume":"28","author":"Wang","year":"2012","journal-title":"Langmuir"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10275","DOI":"10.1073\/pnas.1804548115","article-title":"Interferometric Plasmonic Imaging and Detection of Single Exosomes","volume":"115","author":"Yang","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the Sequence Specificities of DNA- and RNA-Binding Proteins by Deep Learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. Biotechnol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15043","DOI":"10.1364\/OE.25.015043","article-title":"Automatic Phase Aberration Compensation for Digital Holographic Microscopy Based on Deep Learning Background Detection","volume":"25","author":"Nguyen","year":"2017","journal-title":"Opt. Express"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1021\/acsnano.7b00105","article-title":"Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning","volume":"11","author":"Ballard","year":"2017","journal-title":"ACS Nano"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1364\/OPTICA.2.000517","article-title":"Learning Approach to Optical Tomography","volume":"2","author":"Kamilov","year":"2015","journal-title":"Optica"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"17466","DOI":"10.1364\/OE.25.017466","article-title":"Object Classification through Scattering Media with Deep Learning on Time Resolved Measurement","volume":"25","author":"Satat","year":"2017","journal-title":"Opt. Express"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13738","DOI":"10.1364\/OE.24.013738","article-title":"Learning-Based Imaging through Scattering Media","volume":"24","author":"Horisaki","year":"2016","journal-title":"Opt. Express"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"21471","DOI":"10.1038\/srep21471","article-title":"Deep Learning in Label-Free Cell Classification","volume":"6","author":"Chen","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Moon, G., Choi, J., Lee, C., Oh, Y., Kim, K.H., and Kim, D. (2020). Machine Learning-Based Design of Meta-Plasmonic Biosensors with Negative Index Metamaterials. Biosens. Bioelectron., 164C.","DOI":"10.1016\/j.bios.2020.112335"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"30625","DOI":"10.1364\/OE.437939","article-title":"Machine Learning-Based Leaky Momentum Prediction of Plasmonic Random Nanosubstrate","volume":"29","author":"Kim","year":"2021","journal-title":"Opt. Express"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101042","DOI":"10.1016\/j.xcrp.2022.101042","article-title":"Machine Learning and Its Applications for Plasmonics in Biology","volume":"3","author":"Moon","year":"2022","journal-title":"Cell Rep. Phys. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9538","DOI":"10.1021\/acs.analchem.9b00683","article-title":"Deep Learning Approach for Enhanced Detection of Surface Plasmon Scattering","volume":"91","author":"Moon","year":"2019","journal-title":"Anal. Chem."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4884","DOI":"10.1021\/acs.analchem.9b04622","article-title":"Multifunctional Detection of Extracellular Vesicles with Surface Plasmon Resonance Microscopy","volume":"92","author":"Yang","year":"2020","journal-title":"Anal. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16289","DOI":"10.1038\/s41598-021-95593-4","article-title":"Deep Learning-Based Single-Shot Phase Retrieval Algorithm for Surface Plasmon Resonance Microscope Based Refractive Index Sensing Application","volume":"11","author":"Thadson","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8992","DOI":"10.1021\/ac501363z","article-title":"Molecular Scale Origin of Surface Plasmon Resonance Biosensors","volume":"86","author":"Yu","year":"2014","journal-title":"Anal. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12419","DOI":"10.1364\/OE.14.012419","article-title":"Nanowire-Based Enhancement of Localized Surface Plasmon Resonance for Highly Sensitive Detection: A Theoretical Study","volume":"14","author":"Kim","year":"2006","journal-title":"Opt. Express"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1364\/JOSAA.23.002307","article-title":"Effect of Resonant Localized Plasmon Coupling on the Sensitivity Enhancement of Nanowire-Based Surface Plasmon Resonance Biosensors","volume":"23","author":"Kim","year":"2006","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1002\/smll.201101840","article-title":"Nanoscale Localization Sampling Based on Nanoantenna Arrays for Super-resolution Imaging of Fluorescent Monomers on Sliding Microtubules","volume":"8","author":"Kim","year":"2012","journal-title":"Small"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.bios.2013.08.008","article-title":"Self-Aligned Colocalization of 3D Plasmonic Nanogap Arrays for Ultra-Sensitive Surface Plasmon Resonance Detection","volume":"51","author":"Oh","year":"2014","journal-title":"Biosens. Bioelectron."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10896","DOI":"10.1021\/acsnano.5b03934","article-title":"Three-Dimensional Superlocalization Imaging of Gliding Mycoplasma mobile by Extraordinary Light Transmission through Arrayed Nanoholes","volume":"9","author":"Lee","year":"2015","journal-title":"ACS Nano"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"13595","DOI":"10.1021\/acsnano.9b08259","article-title":"Quantitative Amplitude and Phase Imaging with Interferometric Plasmonic Microscopy","volume":"13","author":"Yang","year":"2019","journal-title":"ACS Nano"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.1021\/nn4062885","article-title":"Plasmonic Imaging and Detection of Single DNA Molecules","volume":"8","author":"Yu","year":"2014","journal-title":"ACS Nano"},{"key":"ref_43","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Proceedings of the 18th International Conference, Munich, Germany, 5\u20139 October 2015"},{"key":"ref_44","first-page":"893","article-title":"Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images","volume":"Volume 11071","author":"Frangi","year":"2018","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018, Proceedings of the 21st International Conference, Granada, Spain, 16\u201320 September 2018"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and Flexible Image Augmentations. Information, 11.","DOI":"10.3390\/info11020125"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:59:10Z","timestamp":1760129950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,27]]},"references-count":45,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23198100"],"URL":"https:\/\/doi.org\/10.3390\/s23198100","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,27]]}}}