Department of Computer Science, Technische Universität München, Germany; Computer Vision Laboratory, ETH Zürich, Switzerland.
Computer Vision Laboratory, ETH Zürich, Switzerland; Institute of Pharmacology and Toxicology, University of Zürich, Switzerland.
Med Image Anal. 2015 Oct;25(1):86-94. doi: 10.1016/j.media.2015.03.008. Epub 2015 Apr 23.
We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to a probabilistic model. Starting from an overconnected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (μCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of our probabilistic model and we perform experiments on in-vivo magnetic resonance microangiography (μMRA) images of mouse brains. We finally discuss properties of the networks obtained under different tracking and pruning approaches.
我们引入了一种概率方法来提取血管网络，该方法对血管结构施加生理约束。该方法通过求解整数规划来考虑图像证据和血管之间的几何关系，这被证明可以对概率模型进行最大后验（MAP）估计。从一个过连通的网络开始，通过评估图的局部几何形状和全局连通性来修剪血管残端和虚假连接。我们利用脑血管腐蚀铸型的高分辨率微计算机断层扫描（μCT）数据集获得参考网络，并学习我们概率模型的先验分布，我们还对小鼠大脑的体内磁共振微血管造影（μMRA）图像进行了实验。最后，我们讨论了在不同跟踪和修剪方法下获得的网络的性质。
