Chen Jiangang, Li Jiawei, Ding Xin, Chang Cai, Wang Xiaoting, Ta Dean
1 Academy for Engineering and Technology, Fudan University, Shanghai, China.
2 Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.
Ultrason Imaging. 2018 Jul;40(4):232-244. doi: 10.1177/0161734618777262.
Ultrasound measurement of the inferior vena cava (IVC) is widely implemented in the clinic. However, the process is time consuming and labor intensive, because the IVC diameter is continuously changing with respiration. In addition, artificial errors and intra-operator variations are always considerable, making the measurement inconsistent. Research efforts were recently devoted to developing semiautomated methods. But most required an initial identification of the IVC manually. As a first step toward fully automated IVC measurement, in this paper, we present an intelligent technique for automated IVC identification and localization. Forty-eight ultrasound data sets were collected from eight pigs, each of which included two frames in B-mode and color mode (C-mode) collected at the inspiration, and two cine loops in B-mode and C-mode. Static and dynamic automation algorithms were applied to the data sets for identifying and localizing the IVC. The results were evaluated by comparing with the manual measurement of experienced clinicians. The automated approaches successfully identified the IVC in 47 cases (success rate: 97.9%). The automated localization of the IVC is close to the manual counterpart, with the difference within one diameter. The automatically measured diameters are close to those measured manually, with most differences below 15%. It is revealed that the proposed method can automatically identify the IVC with high success rate and localize the IVC with high accuracy. But the study with high accuracy was conducted under good control and without considering difficult cases, which deserve future explorations. The method is a first step toward fully automated IVC measurement, which is suitable for point-of-care applications.
超声测量下腔静脉（IVC）在临床上已广泛应用。然而，该过程耗时且费力，因为IVC直径会随呼吸不断变化。此外，人为误差和操作者内部差异一直相当大，导致测量结果不一致。近期研究致力于开发半自动方法。但大多数方法都需要先手动识别IVC。作为实现IVC全自动测量的第一步，本文提出一种用于IVC自动识别和定位的智能技术。从八头猪身上收集了48个超声数据集，每个数据集包括在吸气时采集的B模式和彩色模式（C模式）下的两帧图像，以及B模式和C模式下的两个动态影像环。将静态和动态自动化算法应用于这些数据集以识别和定位IVC。通过与经验丰富的临床医生的手动测量结果进行比较来评估结果。自动化方法在47例中成功识别出IVC（成功率：97.9%）。IVC的自动定位与手动定位相近，差异在一个直径范围内。自动测量的直径与手动测量的直径相近，大多数差异低于15%。结果表明，所提出的方法能够以高成功率自动识别IVC并以高精度定位IVC。但该高精度研究是在良好控制条件下进行的，未考虑困难情况，这值得未来探索。该方法是迈向IVC全自动测量的第一步，适用于床旁应用。
