mlsanityData to 14 Oct 2026

UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation

arXiv 2603.01461 ↗First posted 2 Mar 2026cs.CVcited by 0

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Abstract (arXiv)

Echocardiography is critical for diagnosing cardiovascular diseases, yet the shortage of skilled sonographers hinders timely patient care, due to high operational difficulties. Consequently, research on automated probe navigation has significant clinical potential. To achieve robust navigation, it is essential to leverage historical scanning information, mimicking how experts rely on past feedback to adjust subsequent maneuvers. Practical scanning data collected from sonographers typically consists of noisy trajectories inherently generated through trial-and-error exploration. However, existing methods typically model this history as a sequential chain, forcing models to overfit these noisy paths, leading to performance degradation on long sequences. In this paper, we propose UltraStar, which reformulates probe navigation from path regression to anchor-based global localization. By establishing a Star Graph, UltraStar treats historical keyframes as spatial anchors connected directly to the current view, explicitly modeling geometric constraints for precise positioning. We further enhance the Star Graph with a semantic-aware sampling strategy that actively selects the representative landmarks from massive history logs, reducing redundancy for accurate anchoring. Extensive experiments on a dataset with over 1.31 million samples demonstrate that UltraStar outperforms baselines and scales better with longer input lengths, revealing a more effective topology for history modeling under noisy exploration. Code is available at https://github.com/LeapLabTHU/UltraStar.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 2 Mar 2026.

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References · 18

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  9. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  10. 2020Automatic Probe Movement Guidance for Freehand Obstetric Ultrasoundcited by 28
  11. 2025CheXWorld: Exploring Image World Modeling for Radiograph Representation Learningcited by 19
  12. 2024Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Modelcited by 16
6 more
  1. 2025EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidancecited by 15
  2. 2024Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-traincited by 7
  3. 2024UltraSeP: Sequence-aware Pre-training for Echocardiography Probe Movement Guidancecited by 3
  4. 2025UltraHiT: A Hierarchical Transformer Architecture for Generalizable Internal Carotid Artery Robotic Ultrasonographycited by 3
  5. 2025UltraDP: Generalizable Carotid Ultrasound Scanning with Force-Aware Diffusion Policycited by 2
  6. 2025VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidancecited by 1

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