SonoSelect: Efficient Ultrasound Perception via Active Probe Exploration

Artificial intelligence-assisted ultrasound scanning enhances diagnostic reliability and efficiency by providing real-time guidance for standardized image acquisition and reducing operator dependence. However, existing reinforcement learning and learning-assisted ultrasound scanning methods typically rely on carefully designed reward functions or extensive interaction data, which limits their generalization ability and stability across different devices, patient populations, and complex clinical scenarios. To address these challenges, we propose an ultrasound vision-language-action model (US-VLA) for automated ultrasound scanning that explicitly encodes clinical semantic goals and generates sequential probe manipulation actions under real-time ultrasound feedback. In particular, we first design an ultrasound-aware expert fusion module to jointly integrate ultrasound observations with auxiliary contextual information, enabling semantic ultrasound feedback to effectively guide the scanning process. Then, we construct US-VLA-Data, a real-world dataset covering liver and kidney examinations, which includes five clinically defined standard planes and comprises 320 expert scanning trajectories with approximately 80,000 synchronized timesteps. Extensive experiments demonstrate that US-VLA achieves competitive performance in ultrasound probe manipulation tasks, indicating its effectiveness and promising generalization within the evaluated abdominal ultrasound setting. The source code is available at https://github.com/VMVLab/US-VLA.

Medical ultrasound (US) imaging is a frontline tool for the diagnosis of kidney diseases. However, traditional freehand imaging procedure suffers from inconsistent, operator-dependent outcomes, lack of 3D localization information, and risks of work-related musculoskeletal disorders. While robotic ultrasound (RUS) systems offer the potential for standardized, operator-independent 3D kidney data acquisition, the existing scanning methods lack the ability to determine the optimal imaging window for efficient imaging. As a result, the scan is often blindly performed with excessive probe footprint, which frequently leads to acoustic shadowing and incomplete organ coverage. Consequently, there is a critical need for a spatially efficient imaging technique that can maximize the kidney coverage through minimum probe footprint. Here, we propose an autonomous workflow to achieve efficient kidney imaging via template-guided optimal pivoting. The system first performs an explorative imaging to generate partial observations of the kidney. This data is then registered to a kidney template to estimate the organ pose. With the kidney localized, the robot executes a fixed-point pivoting sweep where the imaging plane is aligned with the kidney long axis to minimize the probe translation. The proposed method was validated in simulation and in-vivo. Simulation results indicate that a 60% exploration ratio provides optimal balance between kidney localization accuracy and scanning efficiency. In-vivo evaluation on two male subjects demonstrates a kidney localization accuracy up to 7.36 mm and 13.84 degrees. Moreover, the optimal pivoting approach shortened the probe footprint by around 75 mm when compared with the baselines. These results valid our approach of leveraging anatomical templates to align the probe optimally for volumetric sweep.

Ultrasound images vary widely across scanners, operators, and anatomical targets, which often causes models trained in one setting to generalize poorly to new hospitals and clinical conditions. The Foundation Model Challenge for Ultrasound Image Analysis (FMC-UIA) reflects this difficulty by requiring a single model to handle multiple tasks, including segmentation, detection, classification, and landmark regression across diverse organs and datasets. We propose a unified multi-task framework based on a transformer visual encoder from the Qwen3-VL family. Intermediate token features are projected into spatial feature maps and fused using a lightweight multi-scale feature pyramid, enabling both pixel-level predictions and global reasoning within a shared representation. Each task is handled by a small task-specific prediction head, while training uses task-aware sampling and selective loss balancing to manage heterogeneous supervision and reduce task imbalance. Our method is designed to be simple to optimize and adaptable across a wide range of ultrasound analysis tasks. The performance improved from 67% to 85% on the validation set and achieved an average score of 81.84% on the official test set across all tasks. The code is publicly available at: https://github.com/saitejalekkala33/FMCUIA-ISBI.git