Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images
This work addresses a critical problem for vascular examinations and interventions in robotics-assisted surgeries by enhancing artery-vein segmentation accuracy, though it is incremental as it builds on existing segmentation networks.
The study tackled the challenge of distinguishing arteries from veins in ultrasound images by introducing a force sensing guided segmentation approach that leverages their distinct deformability, achieving significant performance improvements in multiple U-shaped networks such as U-Net, Swin-unet, and Transunet.
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.