构建多场强脑神经分割数据集并提出球面网络，提升小而细的脑神经定位精度。
CNsEMD: An Expert-Annotated Multi-Field-Strength MRI Dataset and a Hyperspherical Manifold Network for Multimodal Cranial Nerve Parcellation
- 在单位超球面上建模多模态特征交互，捕捉方向关系而非简单拼接。
- 在202例3T/5T/7T MRI数据上实现优于现有方法的脑神经分割效果。
- 适合神经影像分析、术前规划及多模态医学图像算法研究者使用。
脑神经（CNs）在感觉、运动和自主功能中起关键作用。从多模态磁共振成像（MRI）中精确分割脑神经对神经解剖分析和神经外科规划至关重要。然而，由于脑神经极细、对比度低、呈细长管状且走行复杂，其准确分割仍极具挑战。此外，缺乏公开的专家标注数据集阻碍了基于学习的方法发展与公平评估。本文提出CNsEMD，一个专家标注的多模态脑神经分割数据集，涵盖202名受试者在3T、5T和7T MRI扫描仪上的数据。我们进一步提出投影超球面流形网络（PHM-Net），通过共享超球面嵌入空间中的角度关系来学习跨模态表示。不同于欧氏空间的融合方式，提出的超球面跨模态交互（HCI）模块在单位超球面上实现T1加权（T1w）与方向编码彩色（DEC）表示间的双向特征交换。保幅投影超球面方向表征（PHOR）保留了DEC方向的轴向特性并保持扩散强度。超球面原型分割头（HPSH）通过归一化体素嵌入与可学习类别原型，将角度相似性扩展至体素级分类。在CNsEMD数据集上的大量实验表明，所提PHM-Net显著优于当前最优方法。CNsEMD为多模态脑神经成像建立了可复现的基准，而PHM-Net则提供了一种在多种MRI采集条件下保持几何一致性的脑神经分割方案。
原文摘要 · Abstract (English)
Cranial nerves (CNs) play essential roles in sensory, motor, and autonomic functions. Accurate CN parcellation from multimodal magnetic resonance imaging (MRI) is crucial for neuroanatomical analysis and neurosurgical planning. However, accurate CN parcellation remains extremely challenging because CNs are very small, exhibit low image contrast, and have slender tubular morphologies and complex anatomical trajectories. Moreover, the lack of publicly available, expert-annotated datasets has impeded the development and fair benchmarking of learning-based CN analysis methods. In this work, we introduce CNsEMD, an expert-annotated multimodal dataset for CN parcellation. It comprises data from 202 subjects acquired on 3T, 5T, and 7T MRI scanners. We further propose the projective hyperspherical manifold network (PHM-Net), which learns cross-modal representations by capturing angular relationships in a shared hyperspherical embedding space. Rather than performing multimodal fusion in Euclidean space, the proposed Hyperspherical cross-modal interaction (HCI) module enables bidirectional feature exchange between T1-weighted (T1w) and direction-encoded color (DEC) representations on a unit hypersphere. The Magnitude-preserving projective hyperspherical orientation representation (PHOR) captures the axial nature of DEC orientations while preserving diffusion magnitude. The hyperspherical prototype segmentation head (HPSH) further extends angular similarity to voxel-wise classification using normalized voxel embeddings and learnable class prototypes. Extensive experimental results on the CNsEMD dataset demonstrate the effectiveness of our PHM-Net against state-of-the-art methods. CNsEMD establishes a reproducible benchmark for multimodal CN imaging, while PHM-Net provides a geometry-consistent solution for CN parcellation across diverse MRI acquisitions.
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