# Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion

arXiv: https://arxiv.org/abs/2208.05399 · HTML: https://mlsanity.com/paper/2208.05399/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study](https://mlsanity.com/paper/1607.08371/) (2016)
- [Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback](https://mlsanity.com/paper/2011.00099/) (2020)
- [Quasi-Newton Solver for Robust Non-Rigid Registration](https://mlsanity.com/paper/2004.04322/) (2020)
- [Deformation-Aware Robotic 3D Ultrasound](https://mlsanity.com/paper/2107.08411/) (2021)
- [VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation](https://mlsanity.com/paper/2205.06676/) (2022)
- [Motion-Aware Robotic 3D Ultrasound](https://mlsanity.com/paper/2107.05998/) (2021)
- [Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound Videos](https://mlsanity.com/paper/1803.07195/) (2018)
- [Automatic ultrasound vessel segmentation with deep spatiotemporal context learning](https://mlsanity.com/paper/2111.02461/) (2021)

## Built on by (most cited first, 12 of 12)

- [Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations](https://mlsanity.com/paper/2307.03705/) (2023)
- [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)
- [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)
- [Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning](https://mlsanity.com/paper/2404.09927/) (2024)
- [Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixture](https://mlsanity.com/paper/2401.02539/) (2024)
- [Deep Kernel and Image Quality Estimators for Optimizing Robotic Ultrasound Controller using Bayesian Optimization](https://mlsanity.com/paper/2310.07392/) (2023)
- [Vibration-Based Energy Metric for Restoring Needle Alignment in Autonomous Robotic Ultrasound](https://mlsanity.com/paper/2508.06921/) (2025)
- [SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound](https://mlsanity.com/paper/2604.25646/) (2026)

## Abstract (arXiv)

Robotic ultrasound (US) imaging aims at overcoming some of the limitations of free-hand US examinations, e.g. difficulty in guaranteeing intra- and inter-operator repeatability. However, due to anatomical and physiological variations between patients and relative movement of anatomical substructures, it is challenging to robustly generate optimal trajectories to examine the anatomies of interest, in particular, when they comprise articulated joints. To address this challenge, this paper proposes a vision-based approach allowing autonomous robotic US limb scanning. To this end, an atlas MRI template of a human arm with annotated vascular structures is used to generate trajectories and register and project them onto patients' skin surfaces for robotic US acquisition. To effectively segment and accurately reconstruct the targeted 3D vessel, we make use of spatial continuity in consecutive US frames by incorporating channel attention modules into a U-Net-type neural network. The automatic trajectory generation method is evaluated on six volunteers with various articulated joint angles. In all cases, the system can successfully acquire the planned vascular structure on volunteers' limbs. For one volunteer the MRI scan was also available, which allows the evaluation of the average radius of the scanned artery from US images, resulting in a radius estimation (1.2±0.05~mm) comparable to the MRI ground truth (1.2±0.04~mm).