Bai Mateng, Li Da, Xu Kaiyao, Ouyang Shuyu, Yuan Ding, Zheng Tinghui
Department of Applied Mechanics, Sichuan University, No. 24 South Section 1, Chengdu 610065, China.
Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin 644600, China.
Bioengineering (Basel). 2023 Jan 20;10(2):139. doi: 10.3390/bioengineering10020139.
Post-operative stent morphology of aortic dissection patients is important for performing clinical diagnosis and prognostic assessment. However, stent morphologies still need to be manually measured, which is a process prone to errors, high time consumption and difficulty in exploiting inter-data associations. Herein, we propose a method based on the stepwise combination of basic, non-divisible data sets to quickly obtain morphological parameters with high accuracy.
We performed the 3D reconstruction of 109 post-operative follow-up CT image data from 26 patients using mimics software. By extracting the spatial locations of the basic morphological observation points on the stent, we defined a basic and non-reducible set of observation points. Further, we implemented a fully automatic stent segmentation and an observation point extraction algorithm. We analyzed the stability and accuracy of the algorithms on a test set containing 8 cases and 408 points. Based on this dataset, we calculated three morphological parameters of different complexity for the different spatial structural features exhibited by the stent. Finally, we compared the two measurement schemes in four aspects: data variability, data stability, statistical process complexity and algorithmic error.
The statistical results of the two methods on two low-complexity morphological parameters (spatial position of stent end and vascular stent end-slip volume) show good agreement ( = 26, , < 0.001, = 0.992, = 0.988). The statistics of the proposed method for the morphological parameters of medium complexity (proximal support ring feature diameter and distal support ring feature diameter) avoid the errors caused by manual extraction, and the magnitude of this correction to the traditional method does not exceed 4 mm with an average correction of 1.38 mm. Meanwhile, our proposed automatic observation point extraction method has only 2.2% error rate on the test set, and the average spatial distance from the manually marked observation points is 0.73 mm. Thus, the proposed method is able to rapidly and accurately measure the stent circumferential deflection angle, which is highly complex and cannot be measured using traditional methods.
The proposed method can significantly reduce the statistical observation time and information processing cost compared to the traditional morphological observation methods. Moreover, when new morphological parameters are required, one can quickly and accurately obtain the target parameters by new "combinatorial functions." Iterative modification of the data set itself is avoided.
主动脉夹层患者术后支架形态对于临床诊断和预后评估至关重要。然而，支架形态仍需手动测量，这一过程容易出错、耗时较长且难以挖掘数据间的关联。在此，我们提出一种基于基本的、不可分割数据集逐步组合的方法，以快速高精度地获取形态学参数。
我们使用mimics软件对26例患者的109份术后随访CT图像数据进行三维重建。通过提取支架上基本形态观察点的空间位置，我们定义了一组基本且不可约的观察点。此外，我们实现了全自动的支架分割和观察点提取算法。我们在一个包含8个病例和408个点的测试集上分析了算法的稳定性和准确性。基于该数据集，我们针对支架呈现的不同空间结构特征计算了三种不同复杂度的形态学参数。最后，我们从数据变异性、数据稳定性、统计过程复杂度和算法误差四个方面比较了两种测量方案。
两种方法对两个低复杂度形态学参数（支架末端空间位置和血管支架末端滑移体积）的统计结果显示出良好的一致性（ = 26， ， < 0.001， = 0.992， = 0.988）。所提方法对中等复杂度形态学参数（近端支撑环特征直径和远端支撑环特征直径）的统计避免了手动提取导致的误差，对传统方法的校正幅度不超过4毫米，平均校正值为1.38毫米。同时，我们提出的自动观察点提取方法在测试集上的错误率仅为2.2% , 与手动标记的观察点的平均空间距离为0.73毫米。因此，所提方法能够快速准确地测量支架圆周偏转角，这是高度复杂且传统方法无法测量的。
与传统形态学观察方法相比，所提方法可显著减少统计观察时间和信息处理成本。此外，当需要新的形态学参数时，可以通过新的“组合函数”快速准确地获取目标参数。避免了数据集本身的迭代修改。
