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Currently, the CN regions can be segmented directly using deep learning\u2010based methods. However, existing methods overlook the unique characteristics of CNs, particularly their environmental features and representation in multimodal images that may lead to suboptimal segmentation outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We proposed a dynamic\u2010guided diffusion probability model for CNs segmentation, which enhances segmentation performance by integrating the intrinsic characteristics of CNs. A dynamic\u2010guided mechanism approach called the SE\u2010A\u2010NL module was proposed. The module is capable of addressing both the varying characterization abilities of multimodal images and the long\u2010range connections of CNs within images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Quantitative and qualitative experiments demonstrate that the proposed method surpasses current state\u2010of\u2010the\u2010art approaches, delivering accurate and effective segmentation of five pairs of cranial nerves. Notably, the method outperforms existing techniques in 16 out of the 20 evaluated metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The overall network model effectively integrates multimodal information and anatomical priors by combining multi\u2010channel attention and non\u2010local attention mechanisms, thereby improving CNs segmentation performance. Thorough comparative and ablation studies highlight the superior performance of the proposed method.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70191","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T19:29:42Z","timestamp":1766431782000},"page":"2919-2929","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic\u2010Guided Diffusion Probability Model for Cranial Nerves Segmentation"],"prefix":"10.1002","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8403-1924","authenticated-orcid":false,"given":"Jiawei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology  Hangzhou 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