A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons
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 si...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1628030/full |
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| author | Liya Li Liya Li Ying Hu Ying Hu Xiaojun Wang Pei Sun Tingwei Quan |
| author_facet | Liya Li Liya Li Ying Hu Ying Hu Xiaojun Wang Pei Sun Tingwei Quan |
| author_sort | Liya Li |
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| description | 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 × 192 × 192 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. |
| format | Article |
| id | doaj-art-edcaec9e251b49c0bccc09df167746c2 |
| institution | Kabale University |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-edcaec9e251b49c0bccc09df167746c22025-08-20T03:59:30ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-08-011910.3389/fninf.2025.16280301628030A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axonsLiya Li0Liya Li1Ying Hu2Ying Hu3Xiaojun Wang4Pei Sun5Tingwei Quan6School of Mathematics and Statistics, Hubei University of Education, Wuhan, Hubei, ChinaInstitute of Big Data Analysis and Applied Mathematics, Hubei University of Education, Wuhan, Hubei, ChinaSchool of Mathematics and Statistics, Hubei University of Education, Wuhan, Hubei, ChinaInstitute of Big Data Analysis and Applied Mathematics, Hubei University of Education, Wuhan, Hubei, ChinaKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, ChinaDepartment of Clinical Research Institute, Central People’s Hospital of Zhanjiang, Zhanjiang, Guangdong, ChinaBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, ChinaNeuron 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 × 192 × 192 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.https://www.frontiersin.org/articles/10.3389/fninf.2025.1628030/fullneuron morphologyaxon reconstructiondeep learningneuronal imaging datasetssegmentation network |
| spellingShingle | Liya Li Liya Li Ying Hu Ying Hu Xiaojun Wang Pei Sun Tingwei Quan A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons Frontiers in Neuroinformatics neuron morphology axon reconstruction deep learning neuronal imaging datasets segmentation network |
| title | A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons |
| title_full | A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons |
| title_fullStr | A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons |
| title_full_unstemmed | A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons |
| title_short | A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons |
| title_sort | neuronal imaging dataset for deep learning in the reconstruction of single neuron axons |
| topic | neuron morphology axon reconstruction deep learning neuronal imaging datasets segmentation network |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2025.1628030/full |
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