Jiang Yifeng, Zhuang Zhenwu, Sinusas Albert J, Papademetris Xenophon
Diagnostic Radiology, Yale University, New Haven, CT.
Conf Comput Vis Pattern Recognit Workshops. 2010 Jun 13:178-185. doi: 10.1109/CVPRW.2010.5543593.
The reconstruction of complete vascular trees from medical images has many important applications. Although vessel detection has been extensively investigated, little work has been done on how connect the results to reconstruct the full trees. In this paper, we propose a novel theoretical framework for automatic vessel connection, where the automation is achieved by leveraging constraints from the physiological properties of the vascular trees. In particular, a physiological functional cost for the whole vascular tree is derived and an efficient algorithm is developed to minimize it. The method is generic and can be applied to different vessel detection/segmentation results, e.g. the classic rigid detection method as adopted in this paper. We demonstrate the effectiveness of this method on both 2D and 3D data.
从医学图像中重建完整的血管树有许多重要应用。尽管血管检测已得到广泛研究，但在如何将检测结果连接起来以重建完整血管树方面所做的工作很少。在本文中，我们提出了一种用于自动血管连接的新颖理论框架，其中通过利用血管树生理特性的约束来实现自动化。具体而言，推导了整个血管树的生理功能代价，并开发了一种高效算法来使其最小化。该方法具有通用性，可应用于不同的血管检测/分割结果，例如本文采用的经典刚性检测方法。我们在二维和三维数据上都证明了该方法的有效性。
