arXiv 2407.213942024-07-31eess.IVcs.CV

基于力感知引导的序贯超声图像动静脉分割

Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images

Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；City University of Hong Kong（香港城市大学）

Yimeng Geng, Gaofeng Meng, Mingcong Chen, Guanglin Cao, Mingyang Zhao, Jianbo Zhao, Hongbin Liu

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出一种力感知引导的超声动静脉分割方法，利用血管形变关键帧与注意力机制提升分割精度，并构建了首个多模态超声动静脉分割数据集Mus-V。

摘要：

在超声图像中准确识别动脉和静脉对于机器人辅助手术中的血管检查和干预至关重要。然而，由于动脉和静脉在形态上具有相似性，现有的超声血管分割方法在区分二者时面临挑战。为应对这一挑战，本研究提出了一种新颖的力感知引导分割方法，通过利用动脉和静脉在可变形性上的差异来提升动静脉分割精度。所提方法利用力的大小在一组序贯超声图像中识别血管形变最显著的关键帧，随后通过注意力机制将这些关键帧与当前帧进行融合，并依据力的大小分配权重。所提出的力感知引导框架可以无缝集成到多种分割网络中，并在U-Net、Swin-unet和Transunet等多个U形网络上取得了显著的性能提升。此外，我们贡献了首个同时包含力数据和图像数据的多模态超声动静脉分割数据集Mus-V。该数据集包含从105段视频中提取的3114张颈动脉和股动脉超声图像，并配有安装在超声探头上的力传感器记录的对应力数据。我们的代码和数据集将公开发布。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。