利用纤维追踪生成粗标签，协同精标注提升颅神经分割精度
Tractography-Guided Dual-Label Collaborative Learning for Multi-Modal Cranial Nerves Parcellation
- 用纤维追踪结果生成粗标签，与专家标注的细标签协同学习
- 在HCP数据集上优于单标签网络，显著提升颅神经分割效果
- 适合医学图像分割、神经解剖研究者参考
颅神经（CNs）的分割是评估其形态特征和解剖路径的重要定量方法。多模态分割网络结合结构磁共振成像（MRI）与扩散MRI，已取得良好分割性能。然而，现有方法对扩散MRI信息挖掘不足，导致融合效果不佳。本文提出一种基于纤维追踪引导的双标签协同学习网络（DCLNet），引入通过颅神经图谱获得的粗略标签，并与专家标注的精确标签进行协同学习。同时，设计模态自适应编码模块（MEM），实现结构MRI与扩散MRI间的信息软交换。在公开的人类连接组计划（HCP）数据集上开展的大量实验表明，该方法优于单标签网络。系统性验证证实了双标签策略在解决颅神经分割固有模糊性方面的有效性。
原文摘要 · Abstract (English)
The parcellation of Cranial Nerves (CNs) serves as a crucial quantitative methodology for evaluating the morphological characteristics and anatomical pathways of specific CNs. Multi-modal CNs parcellation networks have achieved promising segmentation performance, which combine structural Magnetic Resonance Imaging (MRI) and diffusion MRI. However, insufficient exploration of diffusion MRI information has led to low performance of existing multi-modal fusion. In this work, we propose a tractography-guided Dual-label Collaborative Learning Network (DCLNet) for multi-modal CNs parcellation. The key contribution of our DCLNet is the introduction of coarse labels of CNs obtained from fiber tractography through CN atlas, and collaborative learning with precise labels annotated by experts. Meanwhile, we introduce a Modality-adaptive Encoder Module (MEM) to achieve soft information swapping between structural MRI and diffusion MRI. Extensive experiments conducted on the publicly available Human Connectome Project (HCP) dataset demonstrate performance improvements compared to single-label network. This systematic validation underscores the effectiveness of dual-label strategies in addressing inherent ambiguities in CNs parcellation tasks.
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