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Accurate segmentation of the vertebrobasilar artery can provide physicians with more intuitive spatial location relationships, effectively improving the success rate of MVD. In recent years, various learning\u2010based methods have been widely used for artery segmentation. However, accurately segment the vertebrobasilar artery in complex skull base structures with little labeling data remains a\u00a0challenge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study seeks to identify the vessel responsible for MVD using an anatomical feature\u2010guided semi\u2010supervised automated identification approach in clinical\u00a0data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We proposed a multiscale uncertainty cross\u2010pseudo\u2010labeling network with a residual adaptive attention module. Predictions at different scales are generated through multiple decoder levels, emphasizing the reliable part of the prediction and ignoring the regions with low confidence, so that the prediction results at different scales are consistent. Meanwhile, we designed a module called Res\u2010AdaptiveAttention, which enables the utilization of prior knowledge related to the vertebrobasilar\u00a0artery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experiments indicated that our method could maintain the overall structure of the vertebrobasilar artery with minimal annotation labels, outperforming current semi\u2010supervised methods across various metrics. In terms of the dataset, this paper divided the data into a training set, validation set, and test set, with the number of cases being 58 for the training set, eight for the validation set, and 13 for the test set. Additionally, through the evaluation of clinical data, it can accurately display the lesion location, providing significant clinical application\u00a0value.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our method can accurately segment the vertebrobasilar artery in clinical data, and provide visual anatomical references for its positional relationship with the trigeminal nerve. This work lays a foundation for MVD preoperative planning, while further research will focus on quantifying the positional relationship between the segmented artery and trigeminal nerve at multiple anatomical locations to enhance clinical\u00a0applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70352","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:41:06Z","timestamp":1774489266000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anatomical feature\u2010guided semi\u2010supervised recognition of the vertebrobasilar artery for microvascular decompression"],"prefix":"10.1002","volume":"53","author":[{"given":"Jiawei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering Zhejiang University of Technology Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahao","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Engineering Zhejiang University of Technology Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenqiu","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery The First Affiliated Hospital of Wenzhou Medical University Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqiang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering Zhejiang University of Technology Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiyun","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qichuan","family":"Zhuge","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery The First Affiliated Hospital of Wenzhou Medical University Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiang","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Information Engineering Zhejiang University of Technology Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingrun","family":"Zeng","sequence":"additional","affiliation":[{"name":"Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjing","family":"Feng","sequence":"additional","affiliation":[{"name":"Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3171\/2018.8.JNS1893"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0000000000000927"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/ons\/opy043"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.3171\/2019.3.JNS183174"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.3171\/2018.9.JNS181208"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2950051"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.10.035"},{"key":"e_1_2_8_9_1","doi-asserted-by":"crossref","unstructured":"PhellanR PeixinhoA Falc\u00e3oA ForkertND.Vascular segmentation in TOF MRA images of the brain using a deep convolutional neural network. 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