Pulmonary diseases rank prominently among the principal causes of death worldwide. Curing them will require, among other things, a better understanding of the many complex 3D tree-shaped structures within the pulmonary system, such as airways, arteries, and veins. In theory, they can be modeled using high-resolution image stacks. Unfortunately, standard CNN approaches operating on dense voxel grids are prohibitively expensive. To remedy this, we introduce a point-based approach that preserves graph connectivity of tree skeleton and incorporates an implicit surface representation. It delivers SOTA accuracy at a low computational cost and the resulting models have usable surfaces. Due to the scarcity of publicly accessible data, we have also curated an extensive dataset to evaluate our approach and will make it public.

翻译：肺部疾病位居全球主要死亡原因之列。要治愈这些疾病，除其他因素外，需要更深入地理解肺系统中诸多复杂的三维树状结构，例如气道、动脉和静脉。理论上，这些结构可通过高分辨率图像堆叠进行建模。然而，在密集体素网格上运行的标准卷积神经网络方法成本过高。为解决这一问题，我们提出了一种基于点的方法，该方法保留了树骨架的图连接性，并融合了隐式表面表示。它在低计算成本下达到了最先进的精度，且生成的模型具有可用的表面。鉴于公开数据稀缺，我们还整理了一个大规模数据集以评估该方法，并将公开该数据集。