This paper presents a deep-learning model for deformable registration of ultrasound images at online rates, which we call U-RAFT. As its name suggests, U-RAFT is based on RAFT, a convolutional neural network for estimating optical flow. U-RAFT, however, can be trained in an unsupervised manner and can generate synthetic images for training vessel segmentation models. We propose and compare the registration quality of different loss functions for training U-RAFT. We also show how our approach, together with a robot performing force-controlled scans, can be used to generate synthetic deformed images to significantly expand the size of a femoral vessel segmentation training dataset without the need for additional manual labeling. We validate our approach on both a silicone human tissue phantom as well as on in-vivo porcine images. We show that U-RAFT generates synthetic ultrasound images with 98% and 81% structural similarity index measure (SSIM) to the real ultrasound images for the phantom and porcine datasets, respectively. We also demonstrate that synthetic deformed images from U-RAFT can be used as a data augmentation technique for vessel segmentation models to improve intersection-over-union (IoU) segmentation performance

翻译：本文提出一种可实现在线速率超声图像可变形配准的深度学习模型，命名为U-RAFT。顾名思义，U-RAFT基于用于光流估计的卷积神经网络RAFT，但可进行无监督训练，并能够生成合成图像用于训练血管分割模型。我们提出并比较了不同损失函数下U-RAFT的配准质量。研究还表明，该方法结合力控扫描机器人，可生成合成变形图像，显著扩展股血管分割训练数据集规模且无需额外人工标注。我们在硅胶人体组织模型和活体猪图像上验证了该方法。结果显示，U-RAFT生成的合成超声图像与真实超声图像的结构相似性指数（SSIM）在体模数据集上达到98%，在猪数据集上达到81%。我们还证明，U-RAFT生成的合成变形图像可作为血管分割模型的数据增强技术，有效提升分割性能的交并比（IoU）指标。