{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:14:54Z","timestamp":1768590894726,"version":"3.49.0"},"reference-count":49,"publisher":"American Association for the Advancement of Science (AAAS)","funder":[{"DOI":"10.13039\/100009559","name":"Life Sciences Research Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100009559","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R21NS129093"],"award-info":[{"award-number":["R21NS129093"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R21NS129093"],"award-info":[{"award-number":["R21NS129093"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000055","name":"National Institute on Deafness and Other Communication Disorders","doi-asserted-by":"publisher","award":["R21DC020005"],"award-info":[{"award-number":["R21DC020005"]}],"id":[{"id":"10.13039\/100000055","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Goodnight Early Career Innovators Award"},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R56NS117019"],"award-info":[{"award-number":["R56NS117019"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Intell Comput"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>Light-sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times, enabling high-resolution 3-dimensional imaging of large tissue-cleared samples. Inherent to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, which only illuminates a thin section of the sample. Therefore, substantial efforts are dedicated to identifying slender, nondiffracting beam profiles that yield uniform and high-contrast images. An ongoing debate concerns the identification of optimal illumination beams for different samples: Gaussian, Bessel, Airy patterns, and\/or others. However, comparisons among different beam profiles are challenging as their optimization objectives are often different. Given that our large imaging datasets (approximately 0.5 TB of images per sample) are already analyzed using deep learning models, we envisioned a different approach to the problem by designing an illumination beam tailored to boost the performance of the deep learning model. We hypothesized that integrating the physical LSFM illumination model (after passing it through a variable phase mask) into the training of a cell detection network would achieve this goal. Here, we report that joint optimization continuously updates the phase mask and results in improved image quality for better cell detection. The efficacy of our method is demonstrated through both simulations and experiments that reveal substantial enhancements in imaging quality compared to the traditional Gaussian light sheet. We discuss how designing microscopy systems through a computational approach provides novel insights for advancing optical design that relies on deep learning models for the analysis of imaging datasets.<\/jats:p>","DOI":"10.34133\/icomputing.0095","type":"journal-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T16:08:44Z","timestamp":1716998924000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":7,"title":["Intelligent Beam Optimization for Light-Sheet Fluorescence Microscopy through Deep Learning"],"prefix":"10.34133","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3269-1443","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"first","affiliation":[{"name":"Joint Department of Biomedical Engineering, \rNorth Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA."},{"name":"Comparative Medicine Institute, \rNorth Carolina State University, Raleigh, NC 27695, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3413-1917","authenticated-orcid":false,"given":"Mani Ratnam","family":"Rai","sequence":"additional","affiliation":[{"name":"Joint Department of Biomedical Engineering, \rNorth Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA."},{"name":"Comparative Medicine Institute, \rNorth Carolina State University, Raleigh, NC 27695, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7760-211X","authenticated-orcid":false,"given":"Yuheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Joint Department of Biomedical Engineering, \rNorth Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA."},{"name":"Comparative Medicine Institute, \rNorth Carolina State University, Raleigh, NC 27695, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4306-5712","authenticated-orcid":false,"given":"H. Troy","family":"Ghashghaei","sequence":"additional","affiliation":[{"name":"Comparative Medicine Institute, \rNorth Carolina State University, Raleigh, NC 27695, USA."},{"name":"Department of Molecular Biomedical Sciences, \rNorth Carolina State University, Raleigh, NC 27695, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2897-876X","authenticated-orcid":true,"given":"Alon","family":"Greenbaum","sequence":"additional","affiliation":[{"name":"Joint Department of Biomedical Engineering, \rNorth Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA."},{"name":"Comparative Medicine Institute, \rNorth Carolina State University, Raleigh, NC 27695, USA."},{"name":"Bioinformatics Research Center, \rNorth Carolina State University, Raleigh, NC 27695, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"221","published-online":{"date-parts":[[2024,7,4]]},"reference":[{"issue":"1","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1146\/annurev.bioeng.2.1.399","article-title":"Two-photon excitation fluorescence microscopy","volume":"2","author":"So PTC","year":"2000","unstructured":"So PTC, Dong CY, Masters BR, Berland KM. Two-photon excitation fluorescence microscopy. Annu Rev Biomed Eng. 2000;2(1):399\u2013429.","journal-title":"Annu Rev Biomed Eng"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1364\/AOP.10.000111"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12551-020-00773-7"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1364\/AOP.7.000241"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4593"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth929"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1529\/biophysj.106.091116"},{"issue":"5","key":"e_1_3_4_9_2","first-page":"358","article-title":"Resolution and contrast enhancement for lensless digital holographic microscopy and its application in biomedicine","volume":"9","author":"Chen D","year":"2022","unstructured":"Chen D, Wang L, Luo X, Xie H, Chen X. Resolution and contrast enhancement for lensless digital holographic microscopy and its application in biomedicine. Photo-Dermatology. 2022;9(5):358.","journal-title":"Photo-Dermatology"},{"key":"e_1_3_4_10_2","doi-asserted-by":"crossref","first-page":"3839","DOI":"10.1109\/ACCESS.2018.2796646","article-title":"Contrast enhancement of microscopy images using image phase information","volume":"6","author":"Cakir S","year":"2018","unstructured":"Cakir S, Kahraman DC, Cetin-Atalay R, Cetin AE. Contrast enhancement of microscopy images using image phase information. IEEE Access. 2018;6:3839\u20133850.","journal-title":"IEEE Access"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1039\/C8NR06789A"},{"key":"e_1_3_4_12_2","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.optlastec.2012.11.018","article-title":"Image subtraction method for improving lateral resolution and SNR in confocal microscopy","volume":"48","author":"Wang Y","year":"2013","unstructured":"Wang Y, Kuang C, Gu Z, Liu X. Image subtraction method for improving lateral resolution and SNR in confocal microscopy. Opt Laser Technol. 2013;48:489\u2013494.","journal-title":"Opt Laser Technol"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3036"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-020-0560-x"},{"key":"e_1_3_4_15_2","doi-asserted-by":"crossref","unstructured":"Sibarita JB. Deconvolution microscopy: In: Microscopy techniques. Springer: 2005. p. 201\u2013243.","DOI":"10.1007\/b102215"},{"issue":"1","key":"e_1_3_4_16_2","first-page":"20190031","article-title":"The role of artificial intelligence in medical imaging research","volume":"2","author":"Tang X","year":"2019","unstructured":"Tang X. The role of artificial intelligence in medical imaging research. BJR Open. 2019;2(1):20190031.","journal-title":"BJR Open"},{"key":"e_1_3_4_17_2","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.ejrad.2018.06.020","article-title":"The past, present and future role of artificial intelligence in imaging","volume":"105","author":"Fazal MI","year":"2018","unstructured":"Fazal MI, Patel ME, Tye J, Gupta Y. The past, present and future role of artificial intelligence in imaging. Eur J Radiol. 2018;105:246\u2013250.","journal-title":"Eur J Radiol"},{"issue":"5","key":"e_1_3_4_18_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MPUL.2018.2857226","article-title":"Imaging intelligence: AI is transforming medical imaging across the imaging spectrum","volume":"9","author":"Mandal S","year":"2018","unstructured":"Mandal S, Greenblatt AB, An J. Imaging intelligence: AI is transforming medical imaging across the imaging spectrum. IEEE Pulse. 2018;9(5):16\u201324.","journal-title":"IEEE Pulse"},{"issue":"1","key":"e_1_3_4_19_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s12551-018-0449-9","article-title":"Machine learning: Applications of artificial intelligence to imaging and diagnosis","volume":"11","author":"Nichols JA","year":"2019","unstructured":"Nichols JA, Herbert Chan HW, Baker MAB. Machine learning: Applications of artificial intelligence to imaging and diagnosis. Biophys Rev. 2019;11(1):111\u2013118.","journal-title":"Biophys Rev"},{"key":"e_1_3_4_20_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","article-title":"Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19","volume":"14","author":"Shi F","year":"2021","unstructured":"Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng. 2021;14:4\u201315.","journal-title":"IEEE Rev Biomed Eng"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-0853-5"},{"key":"e_1_3_4_22_2","doi-asserted-by":"publisher","DOI":"10.1364\/OE.27.006158"},{"issue":"12","key":"e_1_3_4_23_2","doi-asserted-by":"crossref","first-page":"6351","DOI":"10.1364\/BOE.10.006351","article-title":"Learned sensing: Jointly optimized microscope hardware for accurate image classification","volume":"10","author":"Muthumbi A","year":"2019","unstructured":"Muthumbi A, Chaware A, Kim K, Zhou KC, Konda PC, Chen R, Judkewitz B, Erdmann A, Kappes B, Horstmeyer R. Learned sensing: Jointly optimized microscope hardware for accurate image classification. Biomed Opt Express. 2019;10(12):6351\u20136369.","journal-title":"Biomed Opt Express"},{"key":"e_1_3_4_24_2","doi-asserted-by":"crossref","unstructured":"Wu Y Boominathan V Chen H Sankaranarayanan A Veeraraghavan A. PhaseCam3\u2014Learning phase masks for passive single view depth estimation. In: 2019 IEEE International Conference on Computational Photography (ICCP) Tokyo Japan. IEEE; 2019. p. 1\u201312).","DOI":"10.1109\/ICCPHOT.2019.8747330"},{"issue":"3","key":"e_1_3_4_25_2","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/TCI.2018.2849326","article-title":"Depth estimation from a single image using deep learned phase coded mask","volume":"4","author":"Haim H","year":"2018","unstructured":"Haim H, Elmalem S, Giryes R, Bronstein AM, Marom E. Depth estimation from a single image using deep learned phase coded mask. IEEE Trans Comput Imaging. 2018;4(3):298\u2013310.","journal-title":"IEEE Trans Comput Imaging"},{"issue":"4","key":"e_1_3_4_26_2","first-page":"114","article-title":"End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging","volume":"37","author":"Sitzmann V","year":"2018","unstructured":"Sitzmann V, Diamond S, Peng Y, Dun X, Boyd S, Heidrich W, Heide F, Wetzstein G. End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. CM Trans Graph. 2018;37(4):114.","journal-title":"CM Trans Graph"},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3076873"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.3708"},{"key":"e_1_3_4_29_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-018-0043-4"},{"key":"e_1_3_4_30_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0615-4"},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.402991"},{"key":"e_1_3_4_32_2","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aah6518"},{"issue":"8","key":"e_1_3_4_33_2","doi-asserted-by":"crossref","DOI":"10.1088\/1402-4896\/acd7ae","article-title":"Light-sheet fluorescent microscopy: Fundamentals, developments and application","volume":"98","author":"Kafian H","year":"2023","unstructured":"Kafian H, Mozaffari-Jovin S, Bagheri M, Shaegh SAM. Light-sheet fluorescent microscopy: Fundamentals, developments and application. Phys Scr. 2023;98(8): Article 082001.","journal-title":"Phys Scr"},{"key":"e_1_3_4_34_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-neuro-070918-050357"},{"key":"e_1_3_4_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conb.2011.08.003"},{"issue":"1","key":"e_1_3_4_36_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1364\/BOE.11.000008","article-title":"How to define and optimize axial resolution in light-sheet microscopy: A simulation-based approach","volume":"11","author":"Remacha E","year":"2019","unstructured":"Remacha E, Friedrich L, Vermot J, Fahrbach FO. How to define and optimize axial resolution in light-sheet microscopy: A simulation-based approach. Biomed Opt Express. 2019;11(1):8\u201326.","journal-title":"Biomed Opt Express"},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","DOI":"10.1038\/nphoton.2010.204"},{"key":"e_1_3_4_38_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.1586"},{"issue":"5","key":"e_1_3_4_39_2","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1038\/nmeth.2922","article-title":"Light-sheet microscopy using an Airy beam","volume":"11","author":"Vettenburg T","year":"2014","unstructured":"Vettenburg T, Dalgarno HIC, Nylk J, Coll-Llad\u00f3 C, Ferrier DEK, \u010ci\u017em\u00e1r T, Gunn-Moore FJ, Dholakia K. Light-sheet microscopy using an Airy beam. Nat Methods. 2014;11(5):541\u2013544.","journal-title":"Nat Methods"},{"key":"e_1_3_4_40_2","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.5.003434"},{"issue":"1","key":"e_1_3_4_41_2","doi-asserted-by":"crossref","first-page":"4607","DOI":"10.1038\/s41467-022-32341-w","article-title":"A quantitative analysis of various patterns applied in lattice light sheet microscopy","volume":"13","author":"Shi Y","year":"2022","unstructured":"Shi Y, Daugird TA, Legant WR. A quantitative analysis of various patterns applied in lattice light sheet microscopy. Nat Commun. 2022;13(1):4607.","journal-title":"Nat Commun"},{"key":"e_1_3_4_42_2","volume-title":"Introduction to Fourier optics","author":"Goodman JW","year":"2005","unstructured":"Goodman JW. Introduction to Fourier optics. Englewood (CO): Roberts and Company Publishers; 2005."},{"issue":"1","key":"e_1_3_4_43_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s12859-018-2087-4","article-title":"Assessing microscope image focus quality with deep learning","volume":"19","author":"Yang SJ","year":"2018","unstructured":"Yang SJ, Berndl M, Michael Ando D, Barch M, Narayanaswamy A, Christiansen E, Hoyer S, Roat C, Hung J, Rueden CT, et al. Assessing microscope image focus quality with deep learning. BMC Bioinformatics. 2018;19(1):77.","journal-title":"BMC Bioinformatics"},{"key":"e_1_3_4_44_2","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.454561"},{"key":"e_1_3_4_45_2","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.427099"},{"issue":"8","key":"e_1_3_4_46_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2022.104695","article-title":"Ontogeny of cellular organization and LGR5 expression in porcine cochlea revealed using tissue clearing and 3D imaging","volume":"25","author":"Moatti A","year":"2022","unstructured":"Moatti A, Li C, Sivadanam S, Cai Y, Ranta J, Piedrahita JA, Cheng AG, Ligler FS, Greenbaum A. Ontogeny of cellular organization and LGR5 expression in porcine cochlea revealed using tissue clearing and 3D imaging. iScience. 2022;25(8): Article 104695.","journal-title":"iScience"},{"issue":"2","key":"e_1_3_4_47_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.xpro.2023.102220","article-title":"Tissue clearing and three-dimensional imaging of the whole cochlea and vestibular system from multiple large-animal models","volume":"4","author":"Moatti A","year":"2023","unstructured":"Moatti A, Cai Y, Li C, Popowski KD, Cheng K, Ligler FS, Greenbaum A. Tissue clearing and three-dimensional imaging of the whole cochlea and vestibular system from multiple large-animal models. STAR Protoc. 2023;4(2): Article 102220.","journal-title":"STAR Protoc"},{"issue":"4","key":"e_1_3_4_48_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.crmeth.2023.100454","article-title":"COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains","volume":"3","author":"Cai Y","year":"2023","unstructured":"Cai Y, Zhang X, Li C, Ghashghaei HT, Greenbaum A. COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains. Cell Rep Methods. 2023;3(4): Article 100454.","journal-title":"Cell Rep Methods"},{"issue":"4","key":"e_1_3_4_49_2","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/j.cell.2014.10.010","article-title":"iDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging","volume":"159","author":"Renier N","year":"2014","unstructured":"Renier N, Wu Z, Simon DJ, Yang J, Ariel P, Tessier-Lavigne M. iDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging. Cell. 2014;159(4):896\u2013910.","journal-title":"Cell"},{"key":"e_1_3_4_50_2","unstructured":"Cover TM Thomas JA. Elements of information theory. 2nd edition. Wiley series in telecommunications and signal processing. Hoboken (NJ): John Wiley & Sons Inc.; 2006."}],"container-title":["Intelligent Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.34133\/icomputing.0095","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T01:44:18Z","timestamp":1720057458000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.34133\/icomputing.0095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":49,"alternative-id":["10.34133\/icomputing.0095"],"URL":"https:\/\/doi.org\/10.34133\/icomputing.0095","relation":{},"ISSN":["2771-5892"],"issn-type":[{"value":"2771-5892","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"2024-01-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-22","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"0095"}}