{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:45:48Z","timestamp":1779205548159,"version":"3.51.4"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>Neuron reconstruction is a critical step in quantifying neuronal structures from imaging data. Advances in molecular labeling techniques and optical imaging technologies have spurred extensive research into the patterns of long-range neuronal projections. However, mapping these projections incurs significant costs, as large-scale reconstruction of individual axonal arbors remains time-consuming. In this study, we present a dataset comprising axon imaging volumes along with corresponding annotations to facilitate the evaluation and development of axon reconstruction algorithms. This dataset, derived from 11 mouse brain samples imaged using fluorescence micro-optical sectioning tomography, contains carefully selected 852 volume images sized at 192\u202f\u00d7\u202f192\u202f\u00d7\u202f192 voxels. These images exhibit substantial variations in terms of axon density, image intensity, and signal-to-noise ratios, even within localized regions. Conventional methods often struggle when processing such complex data. To address these challenges, we propose a distance field-supervised segmentation network designed to enhance image signals effectively. Our results demonstrate significantly improved axon detection rates across both state-of-the-art and traditional methodologies. The released dataset and benchmark algorithm provide a data foundation for advancing novel axon reconstruction methods and are valuable for accelerating the reconstruction of long-range axonal projections.<\/jats:p>","DOI":"10.3389\/fninf.2025.1628030","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T05:24:50Z","timestamp":1754371490000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons"],"prefix":"10.3389","volume":"19","author":[{"given":"Liya","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingwei","family":"Quan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,5]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1098\/rstb.2001.0905","article-title":"Generation, description and storage of dendritic morphology data","volume":"356","author":"Ascoli","year":"2001","journal-title":"Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1038\/s42003-022-03320-0","article-title":"Hidden Markov modeling for maximum probability neuron reconstruction","volume":"5","author":"Athey","year":"2022","journal-title":"Commun Biol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s12021-010-9095-5","article-title":"The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions","volume":"9","author":"Brown","year":"2011","journal-title":"Neuroinformatics"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"RP102840","DOI":"10.1101\/2024.09.23.614432","article-title":"PointTree: automatic and accurate reconstruction of long-range axonal projections of single-neuron","volume":"13","author":"Cai","year":"2024","journal-title":"eLife"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/s41593-018-0301-3","article-title":"Panoptic imaging of transparent mice reveals whole-body neuronal projections and skull-meninges connections","volume":"22","author":"Cai","year":"2019","journal-title":"Nat. Neurosci."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"101007","DOI":"10.1016\/j.patter.2024.101007","article-title":"A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction","volume":"5","author":"Chen","year":"2024","journal-title":"Patterns."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"107617","DOI":"10.1016\/j.compbiomed.2023.107617","article-title":"Deep learning in mesoscale brain image analysis: a review","volume":"167","author":"Chen","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"3205","DOI":"10.1109\/TMI.2021.3080695","article-title":"Weakly supervised neuron reconstruction from optical microscopy images with morphological priors","volume":"40","author":"Chen","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s40708-024-00228-9","article-title":"Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity","volume":"11","author":"Choi","year":"2024","journal-title":"Brain Inform."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1038\/nmeth.2481","article-title":"CLARITY for mapping the nervous system","volume":"10","author":"Chung","year":"2013","journal-title":"Nat. Methods"},{"key":"ref11","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"e10566","DOI":"10.7554\/eLife.10566","article-title":"A platform for brain-wide imaging and reconstruction of individual neurons","volume":"5","author":"Economo","year":"2016","journal-title":"eLife"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1523\/ENEURO.0049-14.2015","article-title":"neuTube 1.0: a new Design for Efficient Neuron Reconstruction Software Based on the SWC format","volume":"2","author":"Feng","year":"2015","journal-title":"eNeuro."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1038\/s41586-021-03993-3","article-title":"The mouse cortico-basal ganglia-thalamic network","volume":"598","author":"Foster","year":"2021","journal-title":"Nature"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1038\/s41593-022-01041-5","article-title":"Single-neuron projectome of mouse prefrontal cortex","volume":"25","author":"Gao","year":"2022","journal-title":"Nat. Neurosci."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1926","DOI":"10.1038\/s41592-024-02345-z","article-title":"Gapr for large-scale collaborative single-neuron reconstruction","volume":"21","author":"Gou","year":"2024","journal-title":"Nat. Methods"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.conb.2011.11.010","article-title":"Computational methods and challenges for large-scale circuits mapping","volume":"22","author":"Helmstaedter","year":"2012","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3389\/fnana.2020.00038","article-title":"Weakly supervised learning of 3D deep network for neuron reconstruction","volume":"14","author":"Huang","year":"2020","journal-title":"Front. Neuroanat."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"2450011","DOI":"10.1142\/S1793545824500111","article-title":"A generalized deep neural network approach for improving resolution of fluorescence microscopy images","volume":"17","author":"Jin","year":"2024","journal-title":"J Innovative Optical Health Sci."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","article-title":"A dataset and a technique for generalized nuclear segmentation for computational pathology","volume":"36","author":"Kumar","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3389\/fncir.2017.00051","article-title":"TDat: An efficient platform for processing petabyte-scale whole-brain volumetric images","volume":"11","author":"Li","year":"2017","journal-title":"Front Neural Circuits."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1404","DOI":"10.1126\/science.1191776","article-title":"Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain","volume":"330","author":"Li","year":"2010","journal-title":"Science"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TMI.2017.2679713","article-title":"Deep learning segmentation of optical microscopy images improves 3-D neuron reconstruction","volume":"36","author":"Li","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1038\/s41467-019-09515-0","article-title":"Precise segmentation of densely interweaving neuron clusters using G-cut","volume":"10","author":"Li","year":"2019","journal-title":"Nat. Commun."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"716718","DOI":"10.3389\/fnana.2021.716718","article-title":"Foreground estimation in neuronal images with a sparse-smooth model for robust quantification","volume":"15","author":"Liu","year":"2021","journal-title":"Front. Neuroanat."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"5329","DOI":"10.1093\/bioinformatics\/btac712","article-title":"Neuron tracing from light microscopy images: automation, deep learning and bench testing","volume":"38","author":"Liu","year":"2022","journal-title":"Bioinformatics"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s12021-011-9101-6","article-title":"Neuronal tracing for connectomic studies","volume":"9","author":"Lu","year":"2011","journal-title":"Neuroinformatics"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1038\/s41592-023-01848-5","article-title":"BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets","volume":"20","author":"Manubens-Gil","year":"2023","journal-title":"Nat. Methods"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1002\/cyto.a.20895","article-title":"Neuron tracing in perspective","author":"Meijering","year":"2010","journal-title":"Cytometry A"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1038\/s41586-021-03970-w","article-title":"Cellular anatomy of the mouse primary motor cortex","volume":"598","author":"Munoz-Castaneda","year":"2021","journal-title":"Nature"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1002\/cyto.a.22872","article-title":"NeuronCyto II: An automatic and quantitative solution for crossover neural cells in high throughput screening","volume":"89","author":"Ong","year":"2016","journal-title":"Cytometry A"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1038\/nmeth.2477","article-title":"Mapping brain circuitry with a light microscope","volume":"10","author":"Osten","year":"2013","journal-title":"Nat. Methods"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1016\/j.neuron.2013.03.008","article-title":"Neuronal morphology goes digital: a research hub for cellular and system neuroscience","volume":"77","author":"Parekh","year":"2013","journal-title":"Neuron"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/nmeth.3662","article-title":"NeuroGPS-tree: automatic reconstruction of large-scale neuronal populations with dense neurites","volume":"13","author":"Quan","year":"2016","journal-title":"Nat. Methods"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1093\/bioinformatics\/btw751","article-title":"Automated neuron tracing using probability hypothesis density filtering","volume":"33","author":"Radojevic","year":"2017","journal-title":"Bioinformatics"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TMI.2018.2855736","article-title":"PAT-probabilistic axon tracking for densely labeled neurons in large 3-D micrographs","volume":"38","author":"Skibbe","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","article-title":"Cellpose: a generalist algorithm for cellular segmentation","volume":"18","author":"Stringer","year":"2021","journal-title":"Nat. Methods"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s12021-011-9097-y","article-title":"The past, present, and future of single neuron reconstruction","volume":"9","author":"Svoboda","year":"2011","journal-title":"Neuroinformatics"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"3474","DOI":"10.1038\/s41467-019-11443-y","article-title":"TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain","volume":"10","author":"Wang","year":"2019","journal-title":"Nat. Commun."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s12021-011-9110-5","article-title":"A broadly applicable 3-D neuron tracing method based on open-curve snake","volume":"9","author":"Wang","year":"2011","journal-title":"Neuroinformatics"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"108709","DOI":"10.1016\/j.celrep.2021.108709","article-title":"Chemical sectioning fluorescence tomography: high-throughput, high-contrast, multicolor, whole-brain imaging at subcellular resolution","volume":"34","author":"Wang","year":"2021","journal-title":"Cell Rep."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/j.neuroscience.2005.05.053","article-title":"New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales","volume":"136","author":"Wearne","year":"2005","journal-title":"Neuroscience"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"e13","DOI":"10.1016\/j.cell.2019.07.042","article-title":"Reconstruction of 1,000 projection neurons reveals new cell types and Organization of Long-Range Connectivity in the mouse brain","volume":"179","author":"Winnubst","year":"2019","journal-title":"Cell"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"1448","DOI":"10.1093\/bioinformatics\/btt170","article-title":"APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree","volume":"29","author":"Xiao","year":"2013","journal-title":"Bioinformatics"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"13","DOI":"10.34133\/research.0470","article-title":"From individual to population: circuit Organization of Pyramidal Tract and Intratelencephalic Neurons in mouse sensorimotor cortex","volume":"7","author":"Yao","year":"2024","journal-title":"Research"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s12021-020-09484-6","article-title":"GTree: an open-source tool for dense reconstruction of brain-wide neuronal population","volume":"19","author":"Zhou","year":"2021","journal-title":"Neuroinformatics"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1016\/j.cell.2014.02.023","article-title":"Neural networks of the mouse neocortex","volume":"156","author":"Zingg","year":"2014","journal-title":"Cell"}],"container-title":["Frontiers in Neuroinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2025.1628030\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T05:24:51Z","timestamp":1754371491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2025.1628030\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,5]]},"references-count":47,"alternative-id":["10.3389\/fninf.2025.1628030"],"URL":"https:\/\/doi.org\/10.3389\/fninf.2025.1628030","relation":{},"ISSN":["1662-5196"],"issn-type":[{"value":"1662-5196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,5]]},"article-number":"1628030"}}