Jang Yeonggul, Jung Ho Yub, Hong Youngtaek, Cho Iksung, Shim Hackjoon, Chang Hyuk-Jae
Brain Korea 21 Project for Medical Science, Yonsei University, Seoul 120-752, Republic of Korea.
Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin 449-791, Republic of Korea.
Comput Math Methods Med. 2016;2016:4561979. doi: 10.1155/2016/4561979. Epub 2016 Jan 20.
This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients' CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements.
本文提出了一种从冠状动脉计算机断层扫描血管造影（CCTA）中自动三维分割升主动脉的方法。分割过程分三步进行。首先，通过在霍夫圆上最小化新提出的能量函数来选择初始种子点。其次，通过测地距离变换对升主动脉进行分割。第三，通过一种新颖的传递函数将种子点有效地传递到下一个轴向切片。使用由10名患者的CCTA图像组成的数据库进行实验。对于该实验，医学专家在轴向图像切片上手动标注了真实情况。与最先进的商业主动脉分割算法的比较评估表明，在骰子相似系数（DSC）测量下，我们的方法在计算上更高效且更准确。
