Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:434-437. doi: 10.1109/EMBC48229.2022.9871254.
Accurate measurement of blood vessel diameter on ultrasonic images is important in many vascular exams. In one of them, volumetric blood flow measurements, the volume flow rate is calculated by multiplying the time-averaged velocity with the cross-sectional area of the vessel (using diameter measured from B-mode images). Computation of lumen diameter is also vital for planning surgical procedures like carotid artery stenting and endarterectomy. More recently, several automated vessel diameter estimation methods employing deep learning have been proposed. In this paper, we propose a novel single-step automated deep learning-based vessel diameter estimation technique developed on B-mode images. Longitudinal images of the human common carotid artery were acquired by trained vascular sonographers in human subjects using a linear array probe. Ground truth measurements were obtained by a human expert to validate the proposed technique. 504 images (with augmentation) were divided into training, validation, and test sets. Three pre-trained deep learning networks were used for training, and the lumen diameter was predicted in a hold-out test set. The Mean Absolute Deviation (MAD) and Root Mean Square Error (RMSE) ranged from 0.22-0.65 mm and 0.32-0.82 mm, respectively, for the three networks. Furthermore, 5-fold cross-validation resulted in MAD and RMSE of 0.36±0.1 mm and 0.513±0.15 mm, respectively. Clinical Relevance- The results demonstrate that the technology can potentially be embedded in commercial scanners to make the workflow in vascular ultrasound more efficient.
在许多血管检查中，准确测量超声图像上的血管直径非常重要。在其中一项容积血流测量中，通过将时间平均速度乘以血管的截面积（使用 B 模式图像测量的直径）来计算体积流量。管腔直径的计算对于计划颈动脉支架置入术和内膜切除术等手术程序也至关重要。最近，已经提出了几种使用深度学习的自动血管直径估计方法。在本文中，我们提出了一种新的基于深度学习的单步自动血管直径估计技术，该技术是在 B 模式图像上开发的。通过训练有素的血管超声医师使用线性阵列探头在人体受试者中获得人类颈总动脉的纵向图像。通过人类专家获得真实测量值以验证所提出的技术。504 张图像（带有扩充）被分为训练集、验证集和测试集。使用三个预训练的深度学习网络进行训练，并在独立测试集中预测管腔直径。三个网络的平均绝对偏差（MAD）和均方根误差（RMSE）分别为 0.22-0.65 毫米和 0.32-0.82 毫米。此外，五倍交叉验证的 MAD 和 RMSE 分别为 0.36±0.1 毫米和 0.513±0.15 毫米。临床相关性-结果表明，该技术有可能嵌入商用扫描仪中，使血管超声的工作流程更加高效。
