Ultrasound (US) imaging is clinically used to guide needle insertions because it is safe, real-time, and low cost. The localization of the needle in the ultrasound image, however, remains a challenging problem due to specular reflection off the smooth surface of the needle, speckle noise, and similar line-like anatomical features. This study presents a novel robust needle localization and enhancement algorithm based on deep learning and beam steering methods with three key innovations. First, we employ beam steering to maximize the reflection intensity of the needle, which can help us to detect and locate the needle precisely. Second, we modify the U-Net which is an end-to-end network commonly used in biomedical segmentation by using two branches instead of one in the last up-sampling layer and adding three layers after the last down-sample layer. Thus, the modified U-Net can real-time segment the needle shaft region, detect the needle tip landmark location and determine whether an image frame contains the needle by one shot. Third, we develop a needle fusion framework that employs the outputs of the multi-task deep learning (MTL) framework to precisely locate the needle tip and enhance needle shaft visualization. Thus, the proposed algorithm can not only greatly reduce the processing time, but also significantly increase the needle localization accuracy and enhance the needle visualization for real-time clinical intervention applications.

超声（US）成像在临床上用于引导针头插入，因为它安全，实时且成本低廉。然而，由于在针的光滑表面上的镜面反射，斑点噪声和类似的线状解剖特征，针在超声图像中的定位仍然是一个具有挑战性的问题。这项研究提出了一种新颖的鲁棒的针定位和增强算法，该算法基于深度学习和波束控制方法，具有三项关键创新。首先，我们采用光束转向来最大化针的反射强度，这可以帮助我们精确地检测和定位针。其次，我们修改U-Net，它是生物医学分割中常用的端到端网络，方法是在最后一个上采样层中使用两个分支而不是一个分支，并在最后一个下采样层之后添加三个层。因此，修改后的U-Net可以实时分割针杆区域，检测针尖地标位置并通过一次拍摄确定图像帧是否包含针。第三，我们开发了一种针头融合框架，该框架利用多任务深度学习（MTL）框架的输出来精确定位针尖并增强针杆的可视化效果。因此，所提出的算法不仅可以大大减少处理时间，而且可以显着提高针的定位精度并增强针的可视性，以用于实时临床干预应用。我们开发了一种针头融合框架，该框架利用多任务深度学习（MTL）框架的输出来精确定位针尖并增强针杆的可视化效果。因此，所提出的算法不仅可以大大减少处理时间，而且可以显着提高针的定位精度并增强针的可视性，以用于实时临床干预应用。我们开发了一种针头融合框架，该框架利用多任务深度学习（MTL）框架的输出来精确定位针尖并增强针杆的可视化效果。因此，所提出的算法不仅可以大大减少处理时间，而且可以显着提高针的定位精度并增强针的可视性，以用于实时临床干预应用。