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To address these challenges, we propose SCUX\u2010Net, a novel lightweight convolutional neural network designed to facilitate the segmentation of intracranial aneurysms. SCUX\u2010Net builds upon the 3D UX\u2010Net by introducing two key innovations: (1) a spatial adaptive feature module, integrated before each 3D UX\u2010Net block, enabling multi\u2010scale feature fusion for long\u2010range information interaction; (2) a convolutional block attention module, applied after each downsampling block to emphasize important features across channel and spatial dimensions, suppressing irrelevant information. Experimental results substantiate the effectiveness of SCUX\u2010Net in segmenting intracranial aneurysms on CTA images, achieving a dice similarity coefficient of 80% on the test set. Notably, SCUX\u2010Net excels in detecting small aneurysms (3 mm) and multiple aneurysms, showcasing its potential for clinical\u00a0application.<\/jats:p>","DOI":"10.1049\/ipr2.70209","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T10:05:38Z","timestamp":1758017138000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SCUX\u2010Net: Integrating Multi\u2010Scale Features and Channel\u2010Spatial Attention Model for Intracranial Aneurysm Segmentation"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5313-897X","authenticated-orcid":false,"given":"Aiping","family":"Wu","sequence":"first","affiliation":[{"name":"School of Medical Information Wannan Medical College Wuhu Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingquan","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Medical Information Wannan Medical College Wuhu Anhui China"},{"name":"Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0547-5219","authenticated-orcid":false,"given":"Jiaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Medical Information Wannan Medical College Wuhu Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Medical Information Wannan Medical College Wuhu Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology the First Afffliated Hospital of Wannan Medical College Wuhu China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3171\/2024.2.JNS232752"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00062-020-00979-y"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/0963689718817227"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-63600-3.00012-X"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0b013e3182587839"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0000000000000436"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.117.020342"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1136\/neurintsurg-2020-015824"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-78384-1"},{"issue":"3","key":"e_1_2_10_11_1","first-page":"517","article-title":"Deep Learning\u2010Based Semantic Vessel Graph Extraction for Intracranial Aneurysm Rupture Risk Management","volume":"18","author":"Niemann A.","year":"2023","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.243035126"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3300895"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/math11102344"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12709"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508"},{"key":"e_1_2_10_17_1","doi-asserted-by":"crossref","unstructured":"O.Ronneberger P.Fischer andT.Brox \u201cU\u2010Net: Convolutional Networks for Biomedical Image Segmentation \u201d in18th International Conference on Medical Image computing and Computer\u2010Assisted Intervention\u2013MICCAI 2015(Springer 2015) 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_10_18_1","doi-asserted-by":"crossref","unstructured":"\u00d6.\u00c7i\u00e7ek A.Abdulkadir S. 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