Department of Medical Informatics, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands.
Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands.
Med Image Anal. 2019 Apr;53:104-110. doi: 10.1016/j.media.2019.02.002. Epub 2019 Feb 2.
2D ultrasound (US) image guidance is used in minimally invasive procedures in the liver to visualize the target and the needle. Needle insertion using 2D ultrasound keeping the transducer position to view needle and reach target is challenging. Dedicated needle holders attached to the US transducer help to target in plane and at a specific angle. A drawback of this is that, the probe is fixed to the needle and cannot be rotated to assess the position of the needle in a perpendicular plane. In this study, we propose an automatic needle detection and tracking method using 3D US imaging to improve image guidance and visualization of the target in the liver with respect to the needle during these interventional procedures. The method utilizes a convolutional neural network for detection of the needle in 3D US images. In a subsequent step, the output of the convolutional neural network is used to detect needle candidates, which are fed into a final tracking step to determine the real needle position. The needle position is used to present two perpendicular cross-sectional planes of the 3D US image containing the needle in both directions. Performance of the method was evaluated in phantoms and in-vivo data by calculating the needle position distance and needle orientation angle between segmented needles and reference ground truth needles, which were manually annotated by an observer. The method successfully detects the needle position and orientation with mean errors of 1 mm and 2°, respectively. The proposed method yields a robust automatic needle detection and visualization at a frame rate of 3 Hz in 3D ultrasound imaging of the liver.
2D 超声（US）图像引导用于肝脏的微创程序中，以可视化目标和针。使用 2D 超声保持换能器位置以查看针并到达目标的针插入是具有挑战性的。连接到 US 换能器的专用针夹有助于在平面内和特定角度上瞄准。这样做的一个缺点是，探头固定在针上，不能旋转以评估垂直平面中针的位置。在这项研究中，我们提出了一种使用 3D US 成像的自动针检测和跟踪方法，以改善这些介入程序中针对肝脏中目标的图像引导和可视化。该方法使用卷积神经网络检测 3D US 图像中的针。在后续步骤中，卷积神经网络的输出用于检测针候选物，这些候选物被馈送到最终的跟踪步骤中以确定真实的针位置。针位置用于呈现包含针的两个垂直横截面平面，这两个平面均沿两个方向来自 3D US 图像。通过计算分段针和参考地面真实针之间的针位置距离和针定向角度来评估在体模和体内数据中的方法性能，该距离和角度由观察者手动注释。该方法成功地检测到针的位置和方向，平均误差分别为 1mm 和 2°。该方法在肝脏的 3D 超声成像中以 3Hz 的帧率产生了稳健的自动针检测和可视化。
