{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T19:30:03Z","timestamp":1779391803977,"version":"3.53.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100014857","name":"National Natural Science Foundation of China-Guangdong Joint Fund","doi-asserted-by":"publisher","award":["U20A6005"],"award-info":[{"award-number":["U20A6005"]}],"id":[{"id":"10.13039\/501100014857","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2242022R10089"],"award-info":[{"award-number":["2242022R10089"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1186\/s40708-024-00236-9","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T12:03:33Z","timestamp":1727352213000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy"],"prefix":"10.1186","volume":"11","author":[{"given":"Changshan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liya","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"236_CR1","doi-asserted-by":"publisher","unstructured":"Dodt H-U et al (2007) Apr., Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain, Nat Methods, vol. 4, no. 4, pp. 331\u2013336, https:\/\/doi.org\/10.1038\/nmeth1036","DOI":"10.1038\/nmeth1036"},{"issue":"4","key":"236_CR2","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.cell.2020.01.028","volume":"180","author":"C Kirst","year":"2020","unstructured":"Kirst C et al (2020) Mapping the Fine-Scale Organization and plasticity of the Brain vasculature. Cell 180(4):780\u2013795 .e25, Feb. https:\/\/doi.org\/10.1016\/j.cell.2020.01.028","journal-title":"Cell"},{"key":"236_CR3","doi-asserted-by":"publisher","unstructured":"Silvestri L, Bria A, Sacconi L, Iannello G, Pavone FS (2012) Confocal light sheet microscopy: micron-scale neuroanatomy of the entire mouse brain, Opt. Express, vol. 20, no. 18, p. 20582, Aug. https:\/\/doi.org\/10.1364\/OE.20.020582","DOI":"10.1364\/OE.20.020582"},{"key":"236_CR4","doi-asserted-by":"publisher","unstructured":"Ueda HR, Dodt H-U, Osten P, Economo MN, Chandrashekar J, Keller PJ (May 2020) Whole-brain profiling of cells and circuits in mammals by tissue Clearing and Light-Sheet Microscopy. Neuron 106(3):369\u2013387. https:\/\/doi.org\/10.1016\/j.neuron.2020.03.004","DOI":"10.1016\/j.neuron.2020.03.004"},{"key":"236_CR5","doi-asserted-by":"publisher","unstructured":"Verveer PJ, Swoger J, Pampaloni F, Greger K, Marcello M, Stelzer EHK (2007) High-resolution three-dimensional imaging of large specimens with light sheet\u2013based microscopy, Nat Methods, vol. 4, no. 4, pp. 311\u2013313, Apr. https:\/\/doi.org\/10.1038\/nmeth1017","DOI":"10.1038\/nmeth1017"},{"key":"236_CR6","doi-asserted-by":"publisher","unstructured":"Santi PA (2011) Light Sheet Fluorescence Microscopy: A Review, J Histochem Cytochem., vol. 59, no. 2, pp. 129\u2013138, Feb. https:\/\/doi.org\/10.1369\/0022155410394857","DOI":"10.1369\/0022155410394857"},{"key":"236_CR7","doi-asserted-by":"publisher","unstructured":"Ricci P et al (2022) Jan., Removing striping artifacts in light-sheet fluorescence microscopy: a review, Progress in Biophysics and Molecular Biology, vol. 168, pp. 52\u201365, https:\/\/doi.org\/10.1016\/j.pbiomolbio.2021.07.003","DOI":"10.1016\/j.pbiomolbio.2021.07.003"},{"key":"236_CR8","doi-asserted-by":"publisher","unstructured":"Dong D et al (Oct. 2014) Vertically scanned laser sheet microscopy. J Biomed Opt 19(10):1. https:\/\/doi.org\/10.1117\/1.JBO.19.10.106001","DOI":"10.1117\/1.JBO.19.10.106001"},{"key":"236_CR9","doi-asserted-by":"publisher","unstructured":"Huisken J, Stainier DYR (Sep. 2007) Even fluorescence excitation by multidirectional selective plane illumination microscopy (mSPIM). Opt Lett 32:2608. https:\/\/doi.org\/10.1364\/OL.32.002608","DOI":"10.1364\/OL.32.002608"},{"key":"236_CR10","doi-asserted-by":"publisher","unstructured":"Chang Y, Yan L, Wu T, Zhong S (Dec. 2016) Remote sensing image stripe noise removal: from image decomposition perspective. IEEE Trans Geosci Remote Sens 54(12):7018\u20137031. https:\/\/doi.org\/10.1109\/TGRS.2016.2594080","DOI":"10.1109\/TGRS.2016.2594080"},{"key":"236_CR11","doi-asserted-by":"publisher","unstructured":"M\u00fcnch B, Trtik P, Marone F, Stampanoni M (May 2009) Stripe and ring artifact removal with combined wavelet\u2014fourier filtering. Opt Express 17(10):8567. https:\/\/doi.org\/10.1364\/OE.17.008567","DOI":"10.1364\/OE.17.008567"},{"key":"236_CR12","doi-asserted-by":"publisher","unstructured":"Qu L et al (Jan. 2022) Cross-modal coherent registration of whole mouse brains. Nat Methods 19(1):111\u2013118. https:\/\/doi.org\/10.1038\/s41592-021-01334-w","DOI":"10.1038\/s41592-021-01334-w"},{"key":"236_CR13","doi-asserted-by":"publisher","unstructured":"Tendero Y, Gilles J, Landeau S, Morel JM (2010) Efficient single image non-uniformity correction algorithm, presented at the Security\u2009+\u2009Defence, D. A. Huckridge and R. R. Ebert, Eds., Toulouse, France, Oct. p. 78340E. https:\/\/doi.org\/10.1117\/12.864804","DOI":"10.1117\/12.864804"},{"key":"236_CR14","doi-asserted-by":"publisher","unstructured":"Wang G et al (2019) Jul., DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 7, pp. 1559\u20131572, https:\/\/doi.org\/10.1109\/TPAMI.2018.2840695","DOI":"10.1109\/TPAMI.2018.2840695"},{"key":"236_CR15","doi-asserted-by":"publisher","unstructured":"Alam MS, Wang D, Liao Q, Sowmya A (2023) A Multi-Scale Context Aware Attention Model for Medical Image Segmentation, IEEE J. Biomed. Health Inform., vol. 27, no. 8, pp. 3731\u20133739, Aug. https:\/\/doi.org\/10.1109\/JBHI.2022.3227540","DOI":"10.1109\/JBHI.2022.3227540"},{"key":"236_CR16","doi-asserted-by":"publisher","unstructured":"Hosny KM, Khalid AM, Hamza HM, Mirjalili S (Nov. 2022) Multilevel segmentation of 2D and volumetric medical images using hybrid coronavirus optimization algorithm. Comput Biol Med 150:106003. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106003","DOI":"10.1016\/j.compbiomed.2022.106003"},{"key":"236_CR17","doi-asserted-by":"publisher","unstructured":"Guo Z, Zhao L, Yuan J, Yu H (2022) MSANet: Multiscale Aggregation Network Integrating Spatial and Channel Information for Lung Nodule Detection, IEEE J. Biomed. Health Inform., vol. 26, no. 6, pp. 2547\u20132558, Jun. https:\/\/doi.org\/10.1109\/JBHI.2021.3131671","DOI":"10.1109\/JBHI.2021.3131671"},{"key":"236_CR18","doi-asserted-by":"publisher","unstructured":"Chen X, Yang Q, Wu J, Li H, Tan KC (2023) A hybrid neural coding Approach for Pattern Recognition with spiking neural networks. IEEE Trans Pattern Anal Mach Intell 1\u201315. https:\/\/doi.org\/10.1109\/TPAMI.2023.3339211","DOI":"10.1109\/TPAMI.2023.3339211"},{"key":"236_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-02096-3","author":"CF Park","year":"2023","unstructured":"Park CF et al (2023) Automated neuron tracking inside moving and deforming C. Elegans using deep learning and targeted augmentation. Nat Methods Dec. https:\/\/doi.org\/10.1038\/s41592-023-02096-3","journal-title":"Nat Methods Dec"},{"key":"236_CR20","doi-asserted-by":"publisher","first-page":"9386","DOI":"10.1109\/TIP.2021.3125489","volume":"30","author":"LA Zavala-Mondragon","year":"2021","unstructured":"Zavala-Mondragon LA, De With PHN, Sommen FVD (2021) Image noise reduction based on a fixed Wavelet Frame and CNNs Applied to CT. IEEE Trans Image Process 30:9386\u20139401. https:\/\/doi.org\/10.1109\/TIP.2021.3125489","journal-title":"IEEE Trans Image Process"},{"key":"236_CR21","doi-asserted-by":"publisher","unstructured":"Kuang X, Sui X, Chen Q, Gu G (Aug. 2017) Single infrared image stripe noise removal using deep Convolutional Networks. IEEE Photonics J 9(4):1\u201313. https:\/\/doi.org\/10.1109\/JPHOT.2017.2717948","DOI":"10.1109\/JPHOT.2017.2717948"},{"key":"236_CR22","doi-asserted-by":"publisher","unstructured":"Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising, IEEE Trans. on Image Process., vol. 26, no. 7, pp. 3142\u20133155, Jul. https:\/\/doi.org\/10.1109\/TIP.2017.2662206","DOI":"10.1109\/TIP.2017.2662206"},{"key":"236_CR23","doi-asserted-by":"publisher","first-page":"44544","DOI":"10.1109\/ACCESS.2019.2908720","volume":"7","author":"J Guan","year":"2019","unstructured":"Guan J, Lai R, Xiong A (2019) Wavelet Deep Neural Network for stripe noise removal. IEEE Access 7:44544\u201344554. https:\/\/doi.org\/10.1109\/ACCESS.2019.2908720","journal-title":"IEEE Access"},{"key":"236_CR24","doi-asserted-by":"publisher","unstructured":"Pande-Chhetri R, Abd-Elrahman A (Sep. 2011) De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering. ISPRS J Photogrammetry Remote Sens 66(5):620\u2013636. https:\/\/doi.org\/10.1016\/j.isprsjprs.2011.04.003","DOI":"10.1016\/j.isprsjprs.2011.04.003"},{"key":"236_CR25","doi-asserted-by":"publisher","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the Dimensionality of Data with Neural Networks, Science, vol. 313, no. 5786, pp. 504\u2013507, Jul. https:\/\/doi.org\/10.1126\/science.1127647","DOI":"10.1126\/science.1127647"},{"key":"236_CR26","unstructured":"Ronneberger O, Fischer P, Brox T U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv, May 18, 2015. Accessed: Mar. 16, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/1505.04597"},{"key":"236_CR27","doi-asserted-by":"publisher","unstructured":"Wei Z et al (Mar. 2022) Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network. Biomed Opt Express 13(3):1292. https:\/\/doi.org\/10.1364\/BOE.448838","DOI":"10.1364\/BOE.448838"},{"key":"236_CR28","doi-asserted-by":"publisher","unstructured":"Yi Y et al (Jan. 2024) Mapping of individual sensory nerve axons from digits to spinal cord with the transparent embedding solvent system. Cell Res 34(2):124\u2013139. https:\/\/doi.org\/10.1038\/s41422-023-00867-3","DOI":"10.1038\/s41422-023-00867-3"},{"key":"236_CR29","doi-asserted-by":"publisher","unstructured":"Chen Y, Huang T-Z, Deng L-J, Zhao X-L, Wang M (2017) Group sparsity based regularization model for remote sensing image stripe noise removal, Neurocomputing, vol. 267, pp. 95\u2013106, Dec. https:\/\/doi.org\/10.1016\/j.neucom.2017.05.018","DOI":"10.1016\/j.neucom.2017.05.018"},{"key":"236_CR30","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv, Jun. 21, 2016. Accessed: Mar. 16, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/1606.06650","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"236_CR31","doi-asserted-by":"crossref","unstructured":"Falk T (2019) U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods, 16","DOI":"10.1038\/s41592-019-0356-4"},{"key":"236_CR32","doi-asserted-by":"crossref","unstructured":"Simonyan K, Zisserman A (2015) VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION","DOI":"10.1109\/ICCV.2015.314"},{"key":"236_CR33","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z Rethinking the Inception Architecture for Computer Vision. arXiv, Dec. 11, 2015. Accessed: Apr. 21, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/1512.00567"},{"key":"236_CR34","doi-asserted-by":"publisher","DOI":"10.1101\/2023.06.04.543586","author":"X Qi","year":"2023","unstructured":"Qi X et al (2023) High-throughput confocal airy beam oblique light-sheet tomography of brain-wide imaging at single-cell resolution. Neurosci Preprint Jun. https:\/\/doi.org\/10.1101\/2023.06.04.543586","journal-title":"Neurosci Preprint Jun"},{"key":"236_CR35","doi-asserted-by":"publisher","unstructured":"Burger HC, Schuler CJ, Harmeling S, Image denoising: Can plain neural networks compete with BM3D? in (2012) IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI: IEEE, Jun. 2012, pp. 2392\u20132399. https:\/\/doi.org\/10.1109\/CVPR.2012.6247952","DOI":"10.1109\/CVPR.2012.6247952"},{"key":"236_CR36","doi-asserted-by":"publisher","first-page":"3774","DOI":"10.1109\/TIP.2023.3287735","volume":"32","author":"CF Andersen","year":"2023","unstructured":"Andersen CF, Farup I, Hardeberg JY (2023) Additivity Constrained Linearisation of Camera Calibration Data. IEEE Trans Image Process 32:3774\u20133789. https:\/\/doi.org\/10.1109\/TIP.2023.3287735","journal-title":"IEEE Trans Image Process"},{"key":"236_CR37","doi-asserted-by":"publisher","unstructured":"Kask P, Palo K, Hinnah C, Pommerencke T (2016) Flat field correction for high-throughput imaging of fluorescent samples, Journal of Microscopy, vol. 263, no. 3, pp. 328\u2013340, Sep. https:\/\/doi.org\/10.1111\/jmi.12404","DOI":"10.1111\/jmi.12404"},{"key":"236_CR38","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1109\/TCI.2022.3188413","volume":"8","author":"H Cao","year":"2022","unstructured":"Cao H, Gu X, Zhang M, Zhang H, Chen X (2022) Vignetting correction based on a two-Dimensional Gaussian Filter with harmony for area array sensors. IEEE Trans Comput Imaging 8:576\u2013584. https:\/\/doi.org\/10.1109\/TCI.2022.3188413","journal-title":"IEEE Trans Comput Imaging"},{"key":"236_CR39","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1016\/j.procs.2019.09.207","volume":"159","author":"HM Saleh","year":"2019","unstructured":"Saleh HM, Saad NH, Isa NAM (2019) Overlapping chromosome segmentation using U-Net: Convolutional Networks with Test Time Augmentation. Procedia Comput Sci 159:524\u2013533. https:\/\/doi.org\/10.1016\/j.procs.2019.09.207","journal-title":"Procedia Comput Sci"},{"key":"236_CR40","doi-asserted-by":"publisher","unstructured":"Zhao B-W, Su X-R, Hu P-W, Huang Y-A, You Z-H, Hu L (Aug. 2023) iGRLDTI: an improved graph representation learning method for predicting drug\u2013target interactions over heterogeneous biological information network. Bioinformatics 39(8):btad451. https:\/\/doi.org\/10.1093\/bioinformatics\/btad451","DOI":"10.1093\/bioinformatics\/btad451"},{"key":"236_CR41","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2018) Densely Connected Convolutional Networks. arXiv, Jan. 28, Accessed: May 05, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/1608.06993"},{"issue":"3","key":"236_CR42","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1109\/TNS.1982.4336327","volume":"29","author":"RE Twogood","year":"1982","unstructured":"Twogood RE, Sommer FG (1982) Digital Image Processing. IEEE Trans Nucl Sci 29(3):1075\u20131086. https:\/\/doi.org\/10.1109\/TNS.1982.4336327","journal-title":"IEEE Trans Nucl Sci"},{"key":"236_CR43","doi-asserted-by":"publisher","unstructured":"Sheikh HR, Sabir MF, Bovik AC (2006) A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms, IEEE Trans. on Image Process., vol. 15, no. 11, pp. 3440\u20133451, Nov. https:\/\/doi.org\/10.1109\/TIP.2006.881959","DOI":"10.1109\/TIP.2006.881959"},{"key":"236_CR44","doi-asserted-by":"publisher","unstructured":"Preedanan W, Kondo T, Bunnun P, Kumazawa I (2018) A comparative study of image quality assessment, in 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai: IEEE, Jan. pp. 1\u20134. https:\/\/doi.org\/10.1109\/IWAIT.2018.8369657","DOI":"10.1109\/IWAIT.2018.8369657"},{"key":"236_CR45","doi-asserted-by":"publisher","unstructured":"Zhou G, Zhao Q, Zhang Y, Adali T, Xie S, Cichocki A, Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical, Data (2016) Proc. IEEE, vol. 104, no. 2, pp. 310\u2013331, Feb. https:\/\/doi.org\/10.1109\/JPROC.2015.2474704","DOI":"10.1109\/JPROC.2015.2474704"},{"key":"236_CR46","doi-asserted-by":"publisher","unstructured":"Lai R, Mo Y, Liu Z, Guan J (2019) Local and Nonlocal Steering Kernel Weighted Total Variation Model for Image Denoising, Symmetry, vol. 11, no. 3, p. 329, Mar. https:\/\/doi.org\/10.3390\/sym11030329","DOI":"10.3390\/sym11030329"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-024-00236-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-024-00236-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-024-00236-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T19:10:15Z","timestamp":1732821015000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-024-00236-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,26]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["236"],"URL":"https:\/\/doi.org\/10.1186\/s40708-024-00236-9","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,26]]},"assertion":[{"value":"27 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All animal experiments were approved by the Institutional Animal Care and Use Committee of the Chinese Institute for Brain Research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"24"}}