Pulmonary diseases rank prominently among the principal causes of death worldwide. Curing them will require, among other things, a better understanding of the complex 3D tree-shaped structures within the pulmonary system, such as airways, arteries, and veins. Traditional approaches using high-resolution image stacks and standard CNNs on dense voxel grids face challenges in computational efficiency, limited resolution, local context, and inadequate preservation of shape topology. Our method addresses these issues by shifting from dense voxel to sparse point representation, offering better memory efficiency and global context utilization. However, the inherent sparsity in point representation can lead to a loss of crucial connectivity in tree-shaped structures. To mitigate this, we introduce graph learning on skeletonized structures, incorporating differentiable feature fusion for improved topology and long-distance context capture. Furthermore, we employ an implicit function for efficient conversion of sparse representations into dense reconstructions end-to-end. The proposed method not only delivers state-of-the-art performance in labeling accuracy, both overall and at key locations, but also enables efficient inference and the generation of closed surface shapes. Addressing data scarcity in this field, we have also curated a comprehensive dataset to validate our approach. Data and code are available at https://github.com/M3DV/pulmonary-tree-labeling.

中文翻译：

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通过基于深度点图表示的隐式场对肺树结构进行高效的解剖标记


肺部疾病是全世界死亡的主要原因之一。除其他外，治愈它们需要更好地了解肺系统内复杂的 3D 树形结构，例如气道、动脉和静脉。在密集体素网格上使用高分辨率图像堆栈和标准 CNN 的传统方法面临计算效率、有限分辨率、局部上下文和形状拓扑保留不足方面的挑战。我们的方法通过从密集体素转变为稀疏点表示来解决这些问题，从而提供更好的内存效率和全局上下文利用率。然而，点表示中固有的稀疏性会导致树形结构中关键连通性的丢失。为了缓解这种情况，我们在骨架化结构上引入了图学习，结合可微分特征融合来改进拓扑和远距离上下文捕获。此外，我们采用隐式函数将稀疏表示高效地端到端地转换为密集重建。所提出的方法不仅在整体和关键位置的标记精度方面提供了最先进的性能，而且还实现了高效的推理和闭合表面形状的生成。为了解决该领域的数据稀缺问题，我们还策划了一个全面的数据集来验证我们的方法。数据和代码可在 https://github.com/M3DV/pulmonary-tree-labeling 获取。