Abstract：
Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.
摘要：
3-D中大型和复杂的连体树结构的鲁棒分割是计算机视觉中的主要挑战。在计算生物学中尤其如此，我们经常遇到大的数据结构，但是数量很少，这给学习算法带来了难题。我们证明了将多尺度开口与测地路径传播相结合，可以揭示这个经典的机器视觉挑战，同时通过开发无监督的视觉几何方法（数字拓扑/形态计量学）来规避学习问题。所提出的MSO-GP方法的新颖性来自于由联合结构的骨架引导的测地路径传播，这有助于在该领域的一项特别具有挑战性的任务中实现鲁棒的分割结果。非对比肺计算机断层扫描血管造影照片中的动脉-静脉分离。这是测量血管几何形状以诊断肺部疾病并开发基于图像的表型的重要的第一步。我们首先在合成数据上展示概念验证结果，然后验证在猪肺和人肺数据上的性能，与竞争方法相比，分割时间和用户干预需求更少。
