# Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback

arXiv: https://arxiv.org/abs/2011.00099 · HTML: https://mlsanity.com/paper/2011.00099/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 27 of 27)

- [Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations](https://mlsanity.com/paper/2307.03705/) (2023)
- [VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation](https://mlsanity.com/paper/2205.06676/) (2022)
- [Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion](https://mlsanity.com/paper/2208.05399/) (2022)
- [Skeleton Graph-based Ultrasound-CT Non-rigid Registration](https://mlsanity.com/paper/2305.08228/) (2023)
- [Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation](https://mlsanity.com/paper/2307.03698/) (2023)
- [DefCor-Net: Physics-Aware Ultrasound Deformation Correction](https://mlsanity.com/paper/2308.03865/) (2023)
- [CACTUSS: Common Anatomical CT-US Space for US examinations](https://mlsanity.com/paper/2207.08619/) (2022)
- [Transforming Surgical Interventions with Embodied Intelligence for Ultrasound Robotics](https://mlsanity.com/paper/2406.12651/) (2024)
- [DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal](https://mlsanity.com/paper/2305.08938/) (2023)
- [VibNet: Vibration-Boosted Needle Detection in Ultrasound Images](https://mlsanity.com/paper/2403.14523/) (2024)
- [Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging](https://mlsanity.com/paper/2406.04100/) (2024)
- [Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph](https://mlsanity.com/paper/2307.03800/) (2023)
- [Enhancing Surgical Robots with Embodied Intelligence for Autonomous Ultrasound Scanning](https://mlsanity.com/paper/2405.00461/) (2024)
- [UltrON: Ultrasound Occupancy Networks](https://mlsanity.com/paper/2509.08991/) (2025)
- [Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixture](https://mlsanity.com/paper/2401.02539/) (2024)
- [CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers](https://mlsanity.com/paper/2403.14465/) (2024)
- [OSCAR: Occupancy-based Shape Completion via Acoustic Neural Implicit Representations](https://mlsanity.com/paper/2603.08279/) (2026)
- [Deep Kernel and Image Quality Estimators for Optimizing Robotic Ultrasound Controller using Bayesian Optimization](https://mlsanity.com/paper/2310.07392/) (2023)
- [Bifurcation Identification for Ultrasound-driven Robotic Cannulation](https://mlsanity.com/paper/2409.06817/) (2024)
- [Ultrasound-Guided Real-Time Spinal Motion Visualization for Spinal Instability Assessment](https://mlsanity.com/paper/2602.12917/) (2026)
- [Autonomous Robotic Ultrasound System for Liver Follow-up Diagnosis: Pilot Phantom Study](https://mlsanity.com/paper/2405.05787/) (2024)
- [Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms](https://mlsanity.com/paper/2503.06795/) (2025)
- [Dynamic Exploration on Segment-Proposal Graphs for Tubular Centerline Tracking](https://mlsanity.com/paper/2506.18930/) (2025)
- [PADReg: Physics-Aware Deformable Registration Guided by Contact Force for Ultrasound Sequences](https://mlsanity.com/paper/2508.08685/) (2025)
- [SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound](https://mlsanity.com/paper/2604.25646/) (2026)
- [Scanning While Imagining: A Scene-Graph World Model for Robotic Ultrasound Navigation](https://mlsanity.com/paper/2609.32837/) (2026)
- [Dual-Rate Force-Image Control with Model-Based Orientation Limits for Robotic Ultrasound](https://mlsanity.com/paper/2610.05839/) (2026)

## Abstract (arXiv)

Ultrasound (US) imaging is widely employed for diagnosis and staging of peripheral vascular diseases (PVD), mainly due to its high availability and the fact it does not emit radiation. However, high inter-operator variability and a lack of repeatability of US image acquisition hinder the implementation of extensive screening programs. To address this challenge, we propose an end-to-end workflow for automatic robotic US screening of tubular structures using only the real-time US imaging feedback. We first train a U-Net for real-time segmentation of the vascular structure from cross-sectional US images. Then, we represent the detected vascular structure as a 3D point cloud and use it to estimate the longitudinal axis of the target tubular structure and its mean radius by solving a constrained non-linear optimization problem. Iterating the previous processes, the US probe is automatically aligned to the orientation normal to the target tubular tissue and adjusted online to center the tracked tissue based on the spatial calibration. The real-time segmentation result is evaluated both on a phantom and in-vivo on brachial arteries of volunteers. In addition, the whole process is validated both in simulation and physical phantoms. The mean absolute radius error and orientation error (± SD) in the simulation are 1.16±0.1~mm and 2.7±3.3circ, respectively. On a gel phantom, these errors are 1.95±2.02~mm and 3.3±2.4circ. This shows that the method is able to automatically screen tubular tissues with an optimal probe orientation (i.e. normal to the vessel) and at the same to accurately estimate the mean radius, both in real-time.