# Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms

arXiv: https://arxiv.org/abs/2503.06795 · HTML: https://mlsanity.com/paper/2503.06795/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback](https://mlsanity.com/paper/2011.00099/) (2020)
- [Local Memory Attention for Fast Video Semantic Segmentation](https://mlsanity.com/paper/2101.01715/) (2021)
- [Precise Repositioning of Robotic Ultrasound: Improving Registration-based Motion Compensation using Ultrasound Confidence Optimization](https://mlsanity.com/paper/2208.05383/) (2022)
- [Goal-conditioned reinforcement learning for ultrasound navigation guidance](https://mlsanity.com/paper/2405.01409/) (2024)
- [Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction](https://mlsanity.com/paper/2301.07286/) (2023)
- [RUSOpt: Robotic UltraSound Probe Normalization with Bayesian Optimization for In-plane and Out-plane Scanning](https://mlsanity.com/paper/2310.03406/) (2023)
- [Coaching a Robotic Sonographer: Learning Robotic Ultrasound with Sparse Expert's Feedback](https://mlsanity.com/paper/2409.02337/) (2024)
- [Bifurcation Identification for Ultrasound-driven Robotic Cannulation](https://mlsanity.com/paper/2409.06817/) (2024)

## Abstract (arXiv)

Femoral artery access is essential for numerous clinical procedures, including diagnostic angiography, therapeutic catheterization, and emergency interventions. Despite its critical role, successful vascular access remains challenging due to anatomical variability, overlying adipose tissue, and the need for precise ultrasound (US) guidance. Needle placement errors can result in severe complications, thereby limiting the procedure to highly skilled clinicians operating in controlled hospital environments. While robotic systems have shown promise in addressing these challenges through autonomous scanning and vessel reconstruction, clinical translation remains limited due to reliance on simplified phantom models that fail to capture human anatomical complexity. In this work, we present a method for autonomous robotic US scanning of bifurcated femoral arteries, and validate it on five vascular phantoms created from real patient computed tomography (CT) data. Additionally, we introduce a video-based deep learning US segmentation network tailored for vascular imaging, enabling improved 3D arterial reconstruction. The proposed network achieves a Dice score of 89.21% and an Intersection over Union of 80.54% on a new vascular dataset. The reconstructed artery centerline is evaluated against ground truth CT data, showing an average L2 error of 0.91+/-0.70 mm, with an average Hausdorff distance of 4.36+/-1.11mm. This study is the first to validate an autonomous robotic system for US scanning of the femoral artery on a diverse set of patient-specific phantoms, introducing a more advanced framework for evaluating robotic performance in vascular imaging and intervention.