Automatic segmentation of arterial tree from 3D computed tomographic pulmonary angiography (CTPA) scans
Automatic segmentation of arterial tree from 3D computed tomographic pulmonary angiography (CTPA) scans
复制标题
根据 3D 计算机断层肺血管造影 (CTPA) 扫描自动分割动脉树
DOI：
10.1080/24699322.2019.1649077
复制
发表时间：
2019-08-05
影响因子：
2.1
通讯作者：
Liu, Tongxi
中科院分区：
文献类型：
--
作者：
Zhang, Chi;Sun, Mingxia;Liu, Tongxi
Pulmonary embolism (PE) and other pulmonary vascular diseases, have been found associated with the changes in arterial morphology. To detect arterial changes, we propose a novel, fully automatic method that can extract pulmonary arterial tree in computed tomographic pulmonary angiography (CTPA) images. The approach is based on the fuzzy connectedness framework, combined with 3D vessel enhancement and Harris Corner detection to achieve accurate segmentation. The effectiveness and robustness of the method is validated in clinical datasets consisting of 10 CT angiography scans (6 without PE and 4 with PE). The performance of our method is compared with manual classification and machine learning method based on random forest. Our method achieves a mean accuracy of 92% when compared to manual reference, which is higher than the 89% accuracy achieved by machine learning. This performance of the segmentation for pulmonary arteries may provide a basis for the CAD application of PE.
