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.

中文翻译：

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使用超声自动识别和定位下腔静脉：一项动物研究

下腔静脉 (IVC) 的超声测量在临床中得到广泛应用。然而，该过程既费时又费力，因为 IVC 直径会随着呼吸而不断变化。此外，人为错误和操作员内部的变化总是相当大的，导致测量不一致。最近的研究工作致力于开发半自动化方法。但大多数需要手动对 IVC 进行初始识别。作为实现全自动 IVC 测量的第一步，在本文中，我们提出了一种用于自动 IVC 识别和定位的智能技术。从 8 头猪收集了 48 组超声数据集，每组包括在吸气时收集的 B 模式和彩色模式（C 模式）中的两个帧，以及 B 模式和 C 模式中的两个电影循环。静态和动态自动化算法应用于数据集，用于识别和定位 IVC。通过与有经验的临床医生的手动测量进行比较来评估结果。自动化方法成功识别了 47 例 IVC（成功率：97.9%）。IVC 的自动定位与手动定位接近，差异在一个直径内。自动测量的直径接近于手动测量的直径，大部分差异低于 15%。结果表明，所提出的方法可以自动识别下腔静脉，成功率高，定位下腔静脉准确度高。但这项高精度的研究是在良好的控制下进行的，没有考虑疑难病例，值得未来探索。