Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.

中文翻译：

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具有深度学习的超声引导针跟踪：一种具有光声地面实况的新颖方法

准确的针引导对于安全有效的临床诊断和治疗过程至关重要。传统超声 (US) 引导的针插入经常遇到一致性和精确可视化针的挑战，因此需要开发可靠的方法来跟踪针。作为图像处理中的强大工具，深度学习已显示出增强美国图像中针可见度的希望，尽管它对手动注释或模拟数据作为基本事实的依赖可能会导致潜在的偏差或难以推广到真实的美国图像。光声 (PA) 成像已证明其具有高对比度针可视化的能力。在这项研究中，我们探索了 PA 成像作为深度学习网络训练的可靠基础事实的潜力，而无需专家注释。我们的网络（UIU-Net）经过体外组织图像数据集的训练，在美国图像中定位针头方面表现出了非凡的精度。针分割性能的评估涵盖了以前未见过的离体数据和体内人体数据（从开源数据存储库收集）。具体来说，对于人体数据，修正豪斯多夫距离（MHD）值约为3.73，目标误差值约为2.03，表明预测针位置与实际针位置之间的相似性强且针方向偏差小。我们的方法的一个关键优势是它的适用性超出了从特定成像系统捕获的美国图像，扩展到其他美国成像系统的图像。