Abstract
In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique. However, it is at the expense of the patient and clinician's health due to prolonged radiation exposure. As an alternative, interventional ultrasound has notable benefits such as being radiation-free, fast to deploy, and having a small footprint in the operating room. Yet, ultrasound is hard to interpret, and highly prone to artifacts and noise. Additionally, interventional radiologists must undergo extensive training before they become qualified to diagnose and treat patients effectively, leading to a shortage of staff, and a lack of open-source datasets. In this work, we seek to address both problems by introducing a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images, without demanding any labeled data. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism, and is capable of learning feature changes across time and space. To facilitate training, we used synthetic ultrasound data based on physics-driven catheter insertion simulations, and translated the data into a unique CT-Ultrasound common domain, CACTUSS, to improve the segmentation performance. We generated ground truth segmentation masks by computing the optical flow between adjacent frames using FlowNet2, and performed thresholding to obtain a binary map estimate. Finally, we validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms, thus demonstrating its potential for applications to clinical data in the future.
Abstract (translated)
在最小侵入性介入手术中，增强型内窥镜血管造影仍然是最佳成像技术。然而，由于长时间的辐射暴露，患者和临床医生的健康受到影响。作为替代方案，介入超声具有明显的优势，例如它是辐射free的，部署快速，操作室占用空间小。然而，超声很难解释，易出现伪像和噪声。此外，介入放射科医生在成为有效地诊断和治疗患者之前，必须接受广泛培训，导致缺乏工作人员，缺乏开源数据集。在这项工作中，我们试图通过引入自监督的深度学习架构来解决这两个问题，而不需要任何标记数据。 网络架构基于AiAReSeg，这是使用注意力机制构建的分割转换器，能够学习时间和空间中特征的变化。为了促进训练，我们使用了基于物理仿真引导的穿刺器插入模拟的合成超声数据，并将数据转换为独特的CT-Ultrasound共同领域，CACTUSS，以提高分割性能。我们通过计算相邻帧之间的光流，计算光学流的伪像，并执行阈值以获得二进制图估计。最后，我们在包含未见过的合成数据和从硅动脉幻象收集的图像的测试数据集上验证了我们的模型，从而展示了其在未来应用于临床数据的可能性。
URL
https://arxiv.org/abs/2403.14465
