{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T07:49:43Z","timestamp":1782978583135,"version":"3.54.5"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project","award":["2021ZD0201004"],"award-info":[{"award-number":["2021ZD0201004"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China Grants","doi-asserted-by":"crossref","award":["32192412"],"award-info":[{"award-number":["32192412"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate and efficient neuronal reconstruction is essential for large-scale neuronal projection analysis and neural circuit mapping. However, conventional reconstruction approaches are often constrained by the structural complexity of neurons, the diversity of imaging signals, and variations in annotator expertise, making it difficult to simultaneously achieve high reconstruction quality and efficiency. To address these challenges, this study proposes a quantitative and precision-oriented neuronal reconstruction framework that systematically integrates reconstruction efficiency and accuracy modeling, data\u2013algorithm matching, and refined task allocation strategies. First, mathematical models were established to quantitatively characterize reconstruction efficiency and accuracy, providing a theoretical foundation for precision reconstruction. Based on quantitative indicators of neuronal reconstruction difficulty, a data\u2013algorithm precise matching strategy was developed to adaptively select the most suitable reconstruction method for different types of neuronal data while leveraging the complementary strengths of multiple reconstruction algorithms. Experimental results demonstrated significant improvements in reconstruction accuracy across multiple data categories, with the best-performing image category achieving an accuracy improvement of up to 18.8%. Furthermore, a data\u2013annotator precise allocation strategy was proposed to match data difficulty with annotator capability, enabling efficient human\u2013machine collaborative reconstruction and transforming conventional experience-based reconstruction into a precision-driven quantitative reconstruction paradigm. Compared with traditional reconstruction strategies, the proposed allocation strategy improved reconstruction accuracy by 44.3% and increased overall reconstruction efficiency by 34.6%. In summary, the proposed framework enables quantitative evaluation and controllable assurance of neuronal reconstruction quality. By transforming neuronal reconstruction from a conventional single-method paradigm into a data-driven precision decision-making paradigm, the proposed approach substantially improves reconstruction efficiency while maintaining high reconstruction quality. This work provides reliable methodological support and a solid data foundation for large-scale neuronal morphology analysis and neural circuit research.<\/jats:p>","DOI":"10.1186\/s40708-026-00314-0","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T03:57:03Z","timestamp":1782187023000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A quantitative and precision\u2011oriented neuronal reconstruction approach based on data grading"],"prefix":"10.1186","volume":"13","author":[{"given":"Mingwei","family":"Liao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingming","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"issue":"6","key":"314_CR1","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1016\/j.neuron.2014.08.055","volume":"83","author":"PP Mitra","year":"2014","unstructured":"Mitra PP (2014) The circuit architecture of whole brains at the mesoscopic scale. Neuron 83(6):1273\u20131283","journal-title":"Neuron"},{"issue":"7495","key":"314_CR2","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/nature13186","volume":"508","author":"SW Oh","year":"2014","unstructured":"Oh SW, Harris JA, Ng L, Winslow B, Cain N, Mihalas S, Wang Q, Lau C, Kuan L, Henry AM (2014) A mesoscale connectome of the mouse brain. Nature 508(7495):207\u201314","journal-title":"Nature"},{"issue":"13","key":"314_CR3","doi-asserted-by":"publisher","first-page":"2190","DOI":"10.1002\/cne.24674","volume":"527","author":"MN Economo","year":"2019","unstructured":"Economo MN, Winnubst J, Bas E, Ferreira TA, Chandrashekar J (2019) Single-neuron axonal reconstruction: The search for a wiring diagram of the brain. J Comparative Neurol 527(13):2190\u20132199","journal-title":"J Comparative Neurol"},{"key":"314_CR4","doi-asserted-by":"crossref","unstructured":"Gong H, Zeng S, Yan C, Lv X, Yang Z, Xu T, Feng Z, Ding W, Qi X, Li A (2013) Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution. Neuroimage 74(Complete), 87\u201398","DOI":"10.1016\/j.neuroimage.2013.02.005"},{"issue":"12","key":"314_CR5","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1038\/s41592-018-0184-y","volume":"15","author":"R Lin","year":"2018","unstructured":"Lin R, Wang R, Yuan J, Feng Q, Zhou Y, Zeng S, Ren M, Jiang S, Ni H, Zhou C (2018) Cell-type-specific and projection-specific brain-wide reconstruction of single neurons. Nat Methods 15(12):1033\u20131036","journal-title":"Nat Methods"},{"key":"314_CR6","doi-asserted-by":"crossref","unstructured":"Yuan J, Gong H, Li A, Li X, Shangbin C (2015) Visible rodent brain-wide networks at single-neuron resolution. Frontiers in Neuroanatomy 9(Complete), 70","DOI":"10.3389\/fnana.2015.00070"},{"issue":"24","key":"314_CR7","doi-asserted-by":"publisher","first-page":"5329","DOI":"10.1093\/bioinformatics\/btac712","volume":"38","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wang G, Ascoli GA, Zhou J, Liu L (2022) Neuron tracing from light microscopy images: automation, deep learning and bench testing. Bioinformatics 38(24):5329\u20135339","journal-title":"Bioinformatics"},{"issue":"3","key":"314_CR8","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/s41592-021-01074-x","volume":"18","author":"Q Zhong","year":"2021","unstructured":"Zhong Q, Li A, Jin R, Zhang D, Li X, Jia X, Ding Z, Luo P, Zhou C, Jiang C (2021) High-definition imaging using line-illumination modulation microscopy. Nat Methods 18(3):309\u2013315","journal-title":"Nat Methods"},{"issue":"6009","key":"314_CR9","doi-asserted-by":"publisher","first-page":"1404","DOI":"10.1126\/science.1191776","volume":"330","author":"A Li","year":"2010","unstructured":"Li A, Gong H, Zhang B, Wang Q, Yan C, Wu J, Liu Q, Zeng S, Luo Q (2010) Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science 330(6009):1404\u20131408","journal-title":"Science"},{"issue":"1","key":"314_CR10","doi-asserted-by":"publisher","first-page":"12142","DOI":"10.1038\/ncomms12142","volume":"7","author":"H Gong","year":"2016","unstructured":"Gong H, Xu D, Yuan J, Li X, Guo C, Peng J, Li Y, Schwarz LA, Li A, Hu B (2016) High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nat Commun 7(1):12142","journal-title":"Nat Commun"},{"issue":"1","key":"314_CR11","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.cell.2019.07.042","volume":"179","author":"J Winnubst","year":"2019","unstructured":"Winnubst J, Bas E, Ferreira TA, Wu Z, Chandrashekar J (2019) Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain. Cell 179(1):268\u2013281","journal-title":"Cell"},{"issue":"8","key":"314_CR12","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1038\/nn.2868","volume":"14","author":"M Helmstaedter","year":"2011","unstructured":"Helmstaedter M, Briggman KL, Denk W (2011) High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci 14(8):1081\u20131088","journal-title":"Nat Neurosci"},{"issue":"6","key":"314_CR13","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1016\/j.neuron.2013.03.008","volume":"77","author":"R Parekh","year":"2013","unstructured":"Parekh R, Ascoli G (2013) Neuronal morphology goes digital: A research hub for cellular and system neuroscience. Neuron 77(6):1017\u20131038","journal-title":"Neuron"},{"issue":"4","key":"314_CR14","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.gpb.2019.10.001","volume":"17","author":"A Li","year":"2019","unstructured":"Li A, Guan Y, Gong H, Luo Q (2019) Challenges of processing and analyzing big data in mesoscopic whole-brain imaging. Genomics Proteomics Bioinform 17(4):337\u2013343","journal-title":"Genomics Proteomics Bioinform"},{"key":"314_CR15","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neuroimage.2013.02.005","volume":"74","author":"H Gong","year":"2013","unstructured":"Gong H, Zeng S, Yan C, Lv X, Yang Z, Xu T, Feng Z, Ding W, Qi X, Li A et al (2013) Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution. Neuroimage 74:87\u201398","journal-title":"Neuroimage"},{"issue":"6682","key":"314_CR16","doi-asserted-by":"publisher","first-page":"9198","DOI":"10.1126\/science.adj9198","volume":"383","author":"S Qiu","year":"2024","unstructured":"Qiu S, Hu Y, Huang Y, Gao T, Wang X, Wang D, Ren B, Shi X, Chen Y, Wang X et al (2024) Whole-brain spatial organization of hippocampal single-neuron projectomes. Science 383(6682):9198","journal-title":"Science"},{"issue":"5","key":"314_CR17","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/0895-6111(90)90105-K","volume":"14","author":"JR Glaser","year":"1990","unstructured":"Glaser JR, Glaser EM (1990) Neuron imaging with neurolucida-a pc-based system for image combining microscopy. Comput Med Imaging Graph 14(5):307\u2013317","journal-title":"Comput Med Imaging Graph"},{"issue":"11","key":"314_CR18","doi-asserted-by":"publisher","first-page":"1448","DOI":"10.1093\/bioinformatics\/btt170","volume":"29","author":"H Xiao","year":"2013","unstructured":"Xiao H, Peng H (2013) App2: automatic tracing of 3d neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree. Bioinformatics 29(11):1448\u20131454","journal-title":"Bioinformatics"},{"issue":"3","key":"314_CR19","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s40708-015-0018-y","volume":"2","author":"H Chen","year":"2015","unstructured":"Chen H, Xiao H, Liu T, Peng H (2015) Smarttracing: self-learning-based neuron reconstruction. Brain Inform 2(3):135\u2013144","journal-title":"Brain Inform"},{"issue":"4","key":"314_CR20","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1038\/nbt.1612","volume":"28","author":"H Peng","year":"2010","unstructured":"Peng H, Ruan Z, Long F, Simpson JH, Myers EW (2010) V3d enables real-time 3d visualization and quantitative analysis of large-scale biological image data sets. Nat Biotechnol 28(4):348\u2013353","journal-title":"Nat Biotechnol"},{"issue":"2","key":"314_CR21","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s12021-020-09484-6","volume":"19","author":"H Zhou","year":"2021","unstructured":"Zhou H, Li S, Li A, Huang Q, Xiong F, Li N, Han J, Kang H, Chen Y, Li Y et al (2021) Gtree: an open-source tool for dense reconstruction of brain-wide neuronal population. Neuroinformatics 19(2):305\u2013317","journal-title":"Neuroinformatics"},{"issue":"5\u20136","key":"314_CR22","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1360\/N972018-00998","volume":"64","author":"S Li","year":"2019","unstructured":"Li S, Quan T, Zhou H, Li A, Fu L, Gong H, Luo Q, Zeng S (2019) Review of advances and prospects in neuron reconstruction. Chin Sci Bull 64(5\u20136):532\u2013545","journal-title":"Chin Sci Bull"},{"issue":"4","key":"314_CR23","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1109\/JBHI.2025.3613384","volume":"30","author":"M Liao","year":"2026","unstructured":"Liao M, Chen W, Bao S, Huang G, Gong H, Luo Q, Chen X, Zhou J, Xiao C, Li A (2026) Precise decision energized collaborative strategies to achieve high-quality and large-scale neuronal reconstruction. IEEE J Biomed Health Inform 30(4):2959\u20132972","journal-title":"IEEE J Biomed Health Inform"},{"key":"314_CR24","first-page":"344","volume":"1","author":"D Liu","year":"2009","unstructured":"Liu D, Yu J (2009) Otsu method and k-means. IEEE Computer Society 1:344\u2013349","journal-title":"IEEE Computer Society"},{"key":"314_CR25","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3389\/fnana.2020.00038","volume":"14","author":"Q Huang","year":"2020","unstructured":"Huang Q, Chen Y, Liu S, Xu C, Cao T, Xu Y, Wang X, Rao G, Li A, Zeng S et al (2020) Weakly supervised learning of 3d deep network for neuron reconstruction. Front Neuroanat 14:38","journal-title":"Front Neuroanat"},{"key":"314_CR26","doi-asserted-by":"crossref","unstructured":"Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G, Norajitra T, Wirkert S (2018) nnu-net: Self-adapting framework for u-net-based medical image segmentation.arXiv:1809.10486 arXiv preprint","DOI":"10.1007\/978-3-658-25326-4_7"},{"issue":"1","key":"314_CR27","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/nmeth.3662","volume":"13","author":"T Quan","year":"2016","unstructured":"Quan T, Zhou H, Li J, Li S, Li A, Li Y, Lv X, Luo Q, Gong H, Zeng S (2016) Neurogps-tree: automatic reconstruction of large-scale neuronal populations with dense neurites. Nat Methods 13(1):51\u201354","journal-title":"Nat Methods"},{"key":"314_CR28","doi-asserted-by":"crossref","unstructured":"Feng L, Zhao T, Kim J (2015) neutube 1.0: a new design for efficient neuron reconstruction software based on the swc format. eneuro 2(1)","DOI":"10.1523\/ENEURO.0049-14.2014"},{"issue":"7","key":"314_CR29","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1093\/bioinformatics\/btw751","volume":"33","author":"M Radojevi\u0107","year":"2017","unstructured":"Radojevi\u0107 M, Meijering E (2017) Automated neuron tracing using probability hypothesis density filtering. Bioinformatics 33(7):1073\u20131080","journal-title":"Bioinformatics"},{"key":"314_CR30","doi-asserted-by":"publisher","DOI":"10.3389\/fnana.2021.712842","volume":"15","author":"Q Huang","year":"2021","unstructured":"Huang Q, Cao T, Chen Y, Li A, Zeng S, Quan T (2021) Automated neuron tracing using content-aware adaptive voxel scooping on cnn predicted probability map. Front Neuroanat 15:712842","journal-title":"Front Neuroanat"},{"key":"314_CR31","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.102840","volume":"13","author":"L Cai","year":"2025","unstructured":"Cai L, Fan T, Qu X, Zhang Y, Gou X, Ding Q, Feng W, Cao T, Lv X, Liu X et al (2025) Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain. Elife 13:102840","journal-title":"Elife"},{"issue":"2","key":"314_CR32","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s12021-016-9317-6","volume":"15","author":"S Li","year":"2017","unstructured":"Li S, Zhou H, Quan T, Li J, Li Y, Li A, Luo Q, Gong H, Zeng S (2017) Sparsetracer: the reconstruction of discontinuous neuronal morphology in noisy images. Neuroinformatics 15(2):133\u2013149","journal-title":"Neuroinformatics"},{"issue":"4","key":"314_CR33","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s12021-018-9414-9","volume":"17","author":"S Li","year":"2019","unstructured":"Li S, Quan T, Zhou H, Yin F, Li A, Fu L, Luo Q, Gong H, Zeng S (2019) Identifying weak signals in inhomogeneous neuronal images for large-scale tracing of sparsely distributed neurites. Neuroinformatics 17(4):497\u2013514","journal-title":"Neuroinformatics"},{"issue":"3","key":"314_CR34","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/s41592-021-01074-x","volume":"18","author":"Q Zhong","year":"2021","unstructured":"Zhong Q, Li A, Jin R, Zhang D, Li X, Jia X, Ding Z, Luo P, Zhou C, Jiang C et al (2021) High-definition imaging using line-illumination modulation microscopy. Nat Methods 18(3):309\u2013315","journal-title":"Nat Methods"},{"issue":"5","key":"314_CR35","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1364\/BOE.6.001867","volume":"6","author":"T Yang","year":"2015","unstructured":"Yang T, Zheng T, Shang Z, Wang X, Lv X, Yuan J, Zeng S (2015) Rapid imaging of large tissues using high-resolution stage-scanning microscopy. Biomed Opt Express 6(5):1867\u20131875","journal-title":"Biomed Opt Express"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-026-00314-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-026-00314-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-026-00314-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T07:24:54Z","timestamp":1782977094000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40708-026-00314-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["314"],"URL":"https:\/\/doi.org\/10.1186\/s40708-026-00314-0","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,23]]},"assertion":[{"value":"1 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","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":"27"}}