  Accurate identification of arteries and veins in ultrasound images is crucial
for vascular examinations and interventions in robotics-assisted surgeries.
However, current methods for ultrasound vessel segmentation face challenges in
distinguishing between arteries and veins due to their morphological
similarities. To address this challenge, this study introduces a novel force
sensing guided segmentation approach to enhance artery-vein segmentation
accuracy by leveraging their distinct deformability. Our proposed method
utilizes force magnitude to identify key frames with the most significant
vascular deformation in a sequence of ultrasound images. These key frames are
then integrated with the current frame through attention mechanisms, with
weights assigned in accordance with force magnitude. Our proposed force sensing
guided framework can be seamlessly integrated into various segmentation
networks and achieves significant performance improvements in multiple U-shaped
networks such as U-Net, Swin-unet and Transunet. Furthermore, we contribute the
first multimodal ultrasound artery-vein segmentation dataset, Mus-V, which
encompasses both force and image data simultaneously. The dataset comprises
3114 ultrasound images of carotid and femoral vessels extracted from 105
videos, with corresponding force data recorded by the force sensor mounted on
the US probe. Our code and dataset will be publicly available.
