{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T06:08:37Z","timestamp":1779170917589,"version":"3.51.4"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"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>Vascular network reconstruction is a crucial step in extracting vessel morphology and establishing its topological relationships from biological imaging data, holding significant scientific importance for studying brain structure and function, metabolism, and disease mechanisms. Current methods for vascular network reconstruction typically follow a \u201csegment-first, then reconstruct\u201d pipeline: first generating a binary segmentation from vascular images, followed by topological modeling. However, due to significant variations in vessel diameters, frequent presence of luminal voids in large vessels, and the complex, densely distributed nature of capillaries, existing approaches still face notable limitations in reconstruction accuracy. To address this, this study introduces and releases an annotated dataset of mouse brain vasculature. The dataset comprises 60 3D image blocks with the size of 512 \u00d7 512 \u00d7 512 acquired from four mouse brain samples using fluorescence micro-optical sectioning tomography (fMOST) imaging. It encompasses diverse structural morphologies ranging from large vessels to capillaries, with detailed annotations specifically targeting challenging vascular regions. Additionally, we provide a standardized vascular annotation pipeline and associated tools. This dataset aims to serve as a benchmark to support the development, evaluation, and comparison of algorithms for vascular network segmentation, reconstruction, and related tasks.<\/jats:p>","DOI":"10.3389\/fninf.2026.1809341","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T05:40:10Z","timestamp":1779169210000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A high-resolution dataset of mouse brain vasculature for deep learning-based reconstruction"],"prefix":"10.3389","volume":"20","author":[{"given":"Xinwei","family":"Du","sequence":"first","affiliation":[{"name":"North Alabama International College of Engineering and Technology, Guizhou University, Guiyang","place":["Guizhou, China"]},{"name":"Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang","place":["Guizhou, China"]},{"name":"College of Animal Science, Guizhou University, Guiyang","place":["Guizhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijun","family":"Li","sequence":"additional","affiliation":[{"name":"North Alabama International College of Engineering and Technology, Guizhou University, Guiyang","place":["Guizhou, China"]},{"name":"Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang","place":["Guizhou, China"]},{"name":"College of Animal Science, Guizhou University, Guiyang","place":["Guizhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University","place":["Sanya, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Shen","sequence":"additional","affiliation":[{"name":"Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan","place":["Hubei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingwei","family":"Quan","sequence":"additional","affiliation":[{"name":"Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan","place":["Hubei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/s1056-8719(00)00121-0","article-title":"Techniques to study the pharmacodynamics of isolated large and small blood vessels.","volume":"44","author":"Angus","year":"2000","journal-title":"J. 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