{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T10:47:18Z","timestamp":1786013238381,"version":"3.56.0"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62576335"],"award-info":[{"award-number":["62576335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["KJZD20240903102717023"],"award-info":[{"award-number":["KJZD20240903102717023"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["JCYJ20241202152803005"],"award-info":[{"award-number":["JCYJ20241202152803005"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2025A1515010276"],"award-info":[{"award-number":["2025A1515010276"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010256","name":"Guangzhou Municipal Science and Technology Project","doi-asserted-by":"publisher","award":["2024D03J0010"],"award-info":[{"award-number":["2024D03J0010"]}],"id":[{"id":"10.13039\/501100010256","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.eswa.2026.133453","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T15:03:41Z","timestamp":1783091021000},"page":"133453","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["HiLo: Spatial-spectral hybrid high-low frequency activation for heart and brain vessel segmentation"],"prefix":"10.1016","volume":"332","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1181-5583","authenticated-orcid":false,"given":"Jiahui","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0495-1261","authenticated-orcid":false,"given":"Xin","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0835-3770","authenticated-orcid":false,"given":"Qiong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5649-2193","authenticated-orcid":false,"given":"Valentin","family":"Sinitsyn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1110-4806","authenticated-orcid":false,"given":"Yun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3807-3649","authenticated-orcid":false,"given":"Ying","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8400-3780","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6483-8326","authenticated-orcid":false,"given":"Yan","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.eswa.2026.133453_bib0001","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1016\/j.cell.2022.04.004","article-title":"Atherosclerosis: Recent developments","volume":"185","author":"Bj\u00f6rkegren","year":"2022","journal-title":"Cell"},{"key":"10.1016\/j.eswa.2026.133453_bib0002","unstructured":"Cerebral Artery Segmentation Challenge(2023). Cerebral artery segmentation challenge (CAS) 2023. https:\/\/codalab.lisn.upsaclay.fr\/competitions\/9804#learn_the_details-overview. Accessed: January 10, 2026."},{"key":"10.1016\/j.eswa.2026.133453_bib0003","series-title":"Proceedings of the 31st ACM international conference on multimedia","first-page":"4250","article-title":"Cerebrovascular segmentation in TOF-MRA with topology regularization adversarial model","author":"Chen","year":"2023"},{"issue":"2","key":"10.1016\/j.eswa.2026.133453_bib0004","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/TMI.2022.3184675","article-title":"Generative consistency for semi-supervised cerebrovascular segmentation from TOF-MRA","volume":"42","author":"Chen","year":"2022","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"12","key":"10.1016\/j.eswa.2026.133453_bib0005","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1109\/TMI.2022.3186731","article-title":"Attention-assisted adversarial model for cerebrovascular segmentation in 3D TOF-MRA volumes","volume":"41","author":"Chen","year":"2022","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"5","key":"10.1016\/j.eswa.2026.133453_bib0006","volume":"4","author":"Cury","year":"2022","journal-title":"Radiology: Cardiothoracic Imaging"},{"key":"10.1016\/j.eswa.2026.133453_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102263","article-title":"Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation","volume":"75","author":"Dang","year":"2022","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133453_bib0008","article-title":"Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature","volume":"30","author":"Deshpande","year":"2021","journal-title":"NeuroImage: Clinical"},{"issue":"22","key":"10.1016\/j.eswa.2026.133453_bib0009","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.1016\/j.jacc.2025.08.015","article-title":"Global, regional, and national burden of cardiovascular diseases and risk factors in 204 countries and territories, 1990\u20132023","volume":"86","year":"2025","journal-title":"Journal of the American College of Cardiology"},{"key":"10.1016\/j.eswa.2026.133453_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2026.104040","article-title":"Ace-Protonet: Adaptive covariance eigen-gate and uncertainty-aware prototype learning for coronary artery segmentation","author":"Dong","year":"2026","journal-title":"Medical Image Analysis"},{"issue":"2","key":"10.1016\/j.eswa.2026.133453_bib0011","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1177\/17474930241308142","article-title":"World stroke organization: Global stroke fact sheet 2025","volume":"20","author":"Feigin","year":"2025","journal-title":"International Journal of Stroke"},{"issue":"1072","key":"10.1016\/j.eswa.2026.133453_bib0012","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20160567","article-title":"Image quality in coronary CT angiography: Challenges and technical solutions","volume":"90","author":"Ghekiere","year":"2017","journal-title":"The British Journal of Radiology"},{"key":"10.1016\/j.eswa.2026.133453_bib0013","series-title":"First conference on language modeling","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2024"},{"key":"10.1016\/j.eswa.2026.133453_bib0014","series-title":"International MICCAI brainlesion workshop","first-page":"272","article-title":"Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images","author":"Hatamizadeh","year":"2021"},{"key":"10.1016\/j.eswa.2026.133453_bib0015","first-page":"1","article-title":"MWG-Net: Multiscale wavelet guidance network for covid-19 lung infection segmentation from CT images","volume":"72","author":"Hu","year":"2023","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"5","key":"10.1016\/j.eswa.2026.133453_bib0016","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1109\/TMI.2026.3653779","article-title":"UltraMamba: Mamba-based multimodal ultrasound image adaptive fusion for breast lesion segmentation","volume":"45","author":"Huang","year":"2026","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"9","key":"10.1016\/j.eswa.2026.133453_bib0017","doi-asserted-by":"crossref","first-page":"5839","DOI":"10.1109\/TSMC.2025.3571795","article-title":"Hierarchical network with local-global awareness for ethereum account de-anonymization","volume":"55","author":"Huang","year":"2025","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"10.1016\/j.eswa.2026.133453_bib0018","article-title":"OctMamba: A lightweight ear segmentation framework for 3D portable endoscopic OCT scanner","author":"Huang","year":"2026","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.133453_bib0019","doi-asserted-by":"crossref","first-page":"7410","DOI":"10.1109\/TIFS.2025.3589015","article-title":"SAMamba: Structure-aware mamba for ethereum fraud detection","volume":"20","author":"Huang","year":"2025","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"8","key":"10.1016\/j.eswa.2026.133453_bib0020","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1109\/TMI.2024.3383466","article-title":"Sasan: Spectrum-axial spatial approach networks for medical image segmentation","volume":"43","author":"Huang","year":"2024","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"9","key":"10.1016\/j.eswa.2026.133453_bib0021","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3728632","article-title":"Aortic vessel tree segmentation for cardiovascular diseases treatment: Status quo","volume":"57","author":"Jin","year":"2025","journal-title":"ACM Computing Surveys"},{"issue":"7855","key":"10.1016\/j.eswa.2026.133453_bib0022","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1038\/s41586-021-03392-8","article-title":"The changing landscape of atherosclerosis","volume":"592","author":"Libby","year":"2021","journal-title":"Nature"},{"key":"10.1016\/j.eswa.2026.133453_bib0023","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.eswa.2026.133453_bib0024","unstructured":"Ma, J., Li, F., & Wang, B. (2024). U-Mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv: 2401.04722."},{"key":"10.1016\/j.eswa.2026.133453_bib0025","series-title":"2016 Fourth international conference on 3D vision (3DV)","first-page":"565","article-title":"V-Net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"issue":"12","key":"10.1016\/j.eswa.2026.133453_bib0026","doi-asserted-by":"crossref","first-page":"4442","DOI":"10.1109\/TMI.2024.3424976","article-title":"Costa: A multi-center TOF-MRA dataset and a style self-consistency network for cerebrovascular segmentation","volume":"43","author":"Mou","year":"2024","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.133453_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101874","article-title":"CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging","volume":"67","author":"Mou","year":"2021","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133453_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107648","article-title":"Exploring a frequency-domain attention-guided cascade U-Net: Towards spatially tunable segmentation of vasculature","volume":"167","author":"Mu","year":"2023","journal-title":"Computers in Biology and Medicine"},{"issue":"3","key":"10.1016\/j.eswa.2026.133453_bib0029","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.jcct.2020.11.001","article-title":"SCCT 2021 expert consensus document on coronary computed tomographic angiography: A report of the society of cardiovascular computed tomography","volume":"15","author":"Narula","year":"2021","journal-title":"Journal of Cardiovascular Computed Tomography"},{"key":"10.1016\/j.eswa.2026.133453_bib0030","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"6070","article-title":"Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation","author":"Qi","year":"2023"},{"issue":"6","key":"10.1016\/j.eswa.2026.133453_bib0031","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1109\/TMI.2021.3062280","article-title":"Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT","volume":"40","author":"Qin","year":"2021","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"6","key":"10.1016\/j.eswa.2026.133453_bib0032","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1109\/TMI.2021.3062280","article-title":"Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT","volume":"40","author":"Qin","year":"2021","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"9","key":"10.1016\/j.eswa.2026.133453_bib0033","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1109\/TMI.2024.3398728","article-title":"UNETR++: Delving into efficient and accurate 3D medical image segmentation","volume":"43","author":"Shaker","year":"2024","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.133453_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103442","article-title":"Benefit from public unlabeled data: A frangi filter-based pretraining network for 3D cerebrovascular segmentation","volume":"101","author":"Shi","year":"2025","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133453_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101556","article-title":"Deep vessel segmentation by learning graphical connectivity","volume":"58","author":"Shin","year":"2019","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133453_bib0036","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"16560","article-title":"Cldice-a novel topology-preserving loss function for tubular structure segmentation","author":"Shit","year":"2021"},{"key":"10.1016\/j.eswa.2026.133453_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102724","article-title":"Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images","volume":"84","author":"Su","year":"2023","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133453_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102999","article-title":"AVDNet: Joint coronary artery and vein segmentation with topological consistency","volume":"91","author":"Wang","year":"2024","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133453_bib0039","series-title":"2023\u202fIEEE International conference on bioinformatics and biomedicine (BIBM)","first-page":"1503","article-title":"Leveraging frequency domain learning in 3D vessel segmentation","author":"Wang","year":"2023"},{"key":"10.1016\/j.eswa.2026.133453_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102581","article-title":"3D vessel-like structure segmentation in medical images by an edge-reinforced network","volume":"82","author":"Xia","year":"2022","journal-title":"Medical Image Analysis"},{"issue":"1","key":"10.1016\/j.eswa.2026.133453_bib0041","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TMI.2025.3589797","article-title":"SEGMamba-v2: Long-range sequential modeling mamba for general 3D medical image segmentation","volume":"45","author":"Xing","year":"2025","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.133453_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102287","article-title":"ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images","volume":"109","author":"Zeng","year":"2023","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"1","key":"10.1016\/j.eswa.2026.133453_bib0043","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/TMI.2022.3207093","article-title":"Graph convolution based cross-network multiscale feature fusion for deep vessel segmentation","volume":"42","author":"Zhao","year":"2022","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"11","key":"10.1016\/j.eswa.2026.133453_bib0044","doi-asserted-by":"crossref","first-page":"4156","DOI":"10.1109\/TMI.2025.3568855","article-title":"SA-SEG: Annotation-efficient segmentation for airway tree using saliency-based annotation","volume":"44","author":"Zhou","year":"2025","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.133453_bib0045","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"21085","article-title":"XNet: Wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images","author":"Zhou","year":"2023"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426023626?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426023626?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T10:38:21Z","timestamp":1786012701000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426023626"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":45,"alternative-id":["S0957417426023626"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133453","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"HiLo: Spatial-spectral hybrid high-low frequency activation for heart and brain vessel segmentation","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133453","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133453"}}