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SAH lesions are generally diffusely distributed, showing a variety of scales with irregular edges. The complex characteristics of lesions make SAH segmentation a challenging task. To cope with these difficulties, a u\u2010shaped deformable transformer (UDT) is proposed for SAH segmentation. Specifically, first, a multi\u2010scale deformable attention (MSDA) module is exploited to model the diffuseness and scale\u2010variant characteristics of SAH lesions, where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi\u2010scale features. Second, the cross deformable attention\u2010based skip connection (CDASC) module is designed to model the irregular edge characteristic of SAH lesions, where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder features. Third, the MSDA and CDASC modules are embedded into the backbone Res\u2010UNet to construct the proposed UDT. Extensive experiments are conducted on the self\u2010built SAH\u2010CT dataset and two public medical datasets (GlaS and MoNuSeg). Experimental results show that the presented UDT achieves the state\u2010of\u2010the\u2010art performance.<\/jats:p>","DOI":"10.1049\/cit2.12302","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T01:15:58Z","timestamp":1711415758000},"page":"756-768","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["UDT: U\u2010shaped deformable transformer for subarachnoid haemorrhage image segmentation"],"prefix":"10.1049","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7734-1274","authenticated-orcid":false,"given":"Wei","family":"Xie","sequence":"first","affiliation":[{"name":"Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University  Wuhan China"},{"name":"School of Computer Science Central China Normal University  Wuhan China"},{"name":"National Language Resources Monitoring and Research Center for Network Media Central 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