SIRE：使用图神经网络的尺度不变、旋转等变动脉方向估计
SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks
AI总结：
针对3D医学图像中血管方向估计受尺度、旋转影响的问题，提出融合SO(3)等变与近似尺度不变性的图神经网络SIRE，在多数据集上验证了其跨域泛化的鲁棒血管方向估计能力。
摘要：
3D医学图像中可视化的血管方向是其几何结构的重要描述符，可用于中心线提取以及后续的分割与可视化。动脉存在多种尺度和迂曲程度，确定其精确方向颇具挑战。近期研究已将3D卷积神经网络（CNN）用于该任务，但CNN对血管尺寸和方向的变化较为敏感。我们提出了SIRE：一种用于局部血管方向估计的尺度不变、旋转等变估计器。SIRE采用模块化设计，且因对称性保持而具备泛化能力。\nSIRE由规范等变网格CNN（GEM-CNN，gauge equivariant mesh CNN）构成，该网络并行运行于多个尺寸不同的嵌套球面网格上。每个网格上的特征是对应球体内图像强度的投影。这些特征是球面固有的，与GEM-CNN结合后可实现SO(3)等变性。通过权重共享以及使用对称最大值函数融合多尺度预测，模型可实现近似尺度不变性。因此，SIRE可在具有不同半径的任意方向血管上训练，从而泛化到各种管径和迂曲程度的血管。\n我们使用三个包含不同尺度血管的数据集验证了SIRE的有效性：血管模型库（VMR，vascular model repository）、ASOCA冠状动脉数据集以及一组腹主动脉瘤（AAA，abdominal aortic aneurysm）数据集。我们将SIRE嵌入中心线追踪器中，无论SIRE使用何种数据训练，该追踪器都能准确追踪AAA。此外，即使仅用AAA训练，SIRE也可用于追踪冠状动脉。\n总之，通过融合SO(3)对称性和尺度对称性，SIRE能够确定训练域之外的血管方向，为3D医学图像中的血管几何分析提供了一种鲁棒且数据高效的解决方案。
英文摘要：
Blood vessel orientation as visualized in 3D medical images is an important descriptor of its geometry that can be used for centerline extraction and subsequent segmentation and visualization. Arteries appear at many scales and levels of tortuosity, and determining their exact orientation is challenging. Recent works have used 3D convolutional neural networks (CNNs) for this purpose, but CNNs are sensitive to varying vessel sizes and orientations. We present SIRE: a scale-invariant, rotation-equivariant estimator for local vessel orientation. SIRE is modular and can generalise due to symmetry preservation. SIRE consists of a gauge equivariant mesh CNN (GEM-CNN) operating on multiple nested spherical meshes with different sizes in parallel. The features on each mesh are a projection of image intensities within the corresponding sphere. These features are intrinsic to the sphere and, in combination with the GEM-CNN, lead to SO(3)-equivariance. Approximate scale invariance is achieved by weight sharing and use of a symmetric maximum function to combine multi-scale predictions. Hence, SIRE can be trained with arbitrarily oriented vessels with varying radii to generalise to vessels with a wide range of calibres and tortuosity. We demonstrate the efficacy of SIRE using three datasets containing vessels of varying scales: the vascular model repository (VMR), the ASOCA coronary artery set, and a set of abdominal aortic aneurysms (AAAs). We embed SIRE in a centerline tracker which accurately tracks AAAs, regardless of the data SIRE is trained with. Moreover, SIRE can be used to track coronary arteries, even when trained only with AAAs. In conclusion, by incorporating SO(3) and scale symmetries, SIRE can determine the orientations of vessels outside of the training domain, forming a robust and data-efficient solution to geometric analysis of blood vessels in 3D medical images.
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