Abstract
Purpose: Ultrasound-guided needle interventions are widely used in clinical practice, but their success critically depends on accurate needle placement, which is frequently hindered by the poor and intermittent visibility of needles in ultrasound images. Existing approaches remain limited by artifacts, occlusions, and low contrast, and often fail to support real-time continuous insertion. To overcome these challenges, this study introduces a robust real-time framework for continuous needle detection. Methods: We present ConVibNet, an extension of VibNet for detecting needles with significantly reduced visibility, addressing real-time, continuous needle tracking during insertion. ConVibNet leverages temporal dependencies across successive ultrasound frames to enable continuous estimation of both needle tip position and shaft angle in dynamic scenarios. To strengthen temporal awareness of needle-tip motion, we introduce a novel intersection-and-difference loss that explicitly leverages motion correlations across consecutive frames. In addition, we curated a dedicated dataset for model development and evaluation. Results: The performance of the proposed ConVibNet model was evaluated on our dataset, demonstrating superior accuracy compared to the baseline VibNet and UNet-LSTM models. Specifically, ConVibNet achieved a tip error of 2.80+-2.42 mm and an angle error of 1.69+-2.00 deg. These results represent a 0.75 mm improvement in tip localization accuracy over the best-performing baseline, while preserving real-time inference capability. Conclusion: ConVibNet advances real-time needle detection in ultrasound-guided interventions by integrating temporal correlation modeling with a novel intersection-and-difference loss, thereby improving accuracy and robustness and demonstrating high potential for integration into autonomous insertion systems.
Abstract (translated)
目的：超声引导下的针刺干预在临床上被广泛使用，但其成功与否严重依赖于准确的针头定位，而这一点常因超声图像中针头可见度差且不连续而受阻。现有的方法仍然受限于伪影、遮挡和低对比度问题，并常常无法支持实时连续插入操作。为克服这些挑战，本研究引入了一种稳健的实时框架以实现连续针检测。 方法：我们介绍了ConVibNet，这是VibNet的一种扩展形式，专门用于在显著降低可见性的条件下检测针头，在针刺过程中实现了对针头实时、连续追踪的能力。ConVibNet利用了连续超声帧之间的时序依赖关系来实现在动态场景下同时估计针尖位置和针身角度的持续性计算。为了增强针对针尖运动的时间意识，我们引入了一种新颖的交差损失（intersection-and-difference loss），该方法明确地利用了连续帧间的运动相关性。此外，我们还为模型的发展和评估整理了一个专门的数据集。 结果：我们在我们的数据集上对所提出的ConVibNet模型进行了性能测试，显示其与基准模型VibNet及UNet-LSTM相比具有优越的准确性。具体来说，ConVibNet实现了针尖误差2.80±2.42毫米和角度误差1.69±2.00度的成绩。这些结果在最佳基础线定位精度上提高了0.75毫米，并同时保持了实时推理能力。 结论：通过将时序相关性建模与新颖的交差损失结合，ConVibNet推动了超声引导针刺干预中实时针检测的进步，从而提高了准确性及鲁棒性，并展示了在自主插入系统集成中的高潜力。
URL
https://arxiv.org/abs/2603.01147
