Dynamic-Guided Diffusion Probability Model for Cranial Nerves Segmentation.

Journal: Magnetic resonance in medicine
Published Date: Dec 22, 2025

Abstract

PURPOSE: Segmentation of cranial nerves (CNs) bundles using magnetic resonance imaging (MRI) provides a valuable quantitative approach for analyzing the morphology and orientation of individual CNs. Currently, the CN regions can be segmented directly using deep learning-based 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. METHODS: We proposed a dynamic-guided diffusion probability model for CNs segmentation, which enhances segmentation performance by integrating the intrinsic characteristics of CNs. A dynamic-guided mechanism approach called the SE-A-NL module was proposed. The module is capable of addressing both the varying characterization abilities of multimodal images and the long-range connections of CNs within images. RESULTS: Quantitative and qualitative experiments demonstrate that the proposed method surpasses current state-of-the-art 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. CONCLUSION: The overall network model effectively integrates multimodal information and anatomical priors by combining multi-channel attention and non-local attention mechanisms, thereby improving CNs segmentation performance. Thorough comparative and ablation studies highlight the superior performance of the proposed method.

Authors

  • Jiawei Zhang
    a Department of Pharmacy , Special Drugs R&D Center of People's Armed Police Forces , Logistics University of Chinese People's Armed Police Forces , Tianjin , China.
  • Qingrun Zeng
    Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
  • Jiahao Huang
    Beijing Smart Tree Medical Technology Co. Ltd., No.24, Huangsi Street, Xicheng District, Beijing, 100011, China.
  • Jianzhong He
    College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.
  • Yiang Pan
    College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.
  • Yongqiang Li
    Advanced Pharmaceutics and Drug Delivery Laboratory, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.
  • Lei Xie
    Ph.D. Program in Computer Science, The City University of New York, New York, NY, United States.
  • Yuanjing Feng
    Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China. Electronic address: [email protected].

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