UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation
Teng Wang, Haojun Jiang, Chenxi Li, Diwen Wang, Yihang Tang, Zhenguo Sun, Yujiao Deng, Shiji Song, Gao Huang

TL;DR
UltraStar introduces a semantic-aware star graph model for echocardiography probe navigation, improving robustness and accuracy by leveraging spatial anchors and reducing noise impact in historical data.
Contribution
It reformulates probe navigation as anchor-based localization using a star graph, incorporating semantic-aware sampling to handle noisy historical data effectively.
Findings
Outperforms baseline methods on a large dataset
Scales better with longer input sequences
Provides more accurate and robust navigation results
Abstract
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…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Soft Robotics and Applications · Advanced Neural Network Applications
