{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T01:57:05Z","timestamp":1784771825309,"version":"3.55.0"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82102137"],"award-info":[{"award-number":["82102137"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2122015"],"award-info":[{"award-number":["T2122015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3\u2009h. We also demonstrated that our pipeline could be applied to the vascular analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The dataset is available at http:\/\/atlas.brainsmatics.org\/a\/li2301. The source code is freely available at https:\/\/github.com\/visionlyx\/HP-VSP.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad145","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T02:35:57Z","timestamp":1679452557000},"source":"Crossref","is-referenced-by-count":9,"title":["A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7404-6595","authenticated-orcid":false,"given":"Yuxin","family":"Li","sequence":"first","affiliation":[{"name":"Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi\u2019an University of Technology , Xi\u2019an 710048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuhua","family":"Liu","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory for Network Computing 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