Abstract
Clinical ultrasound acquisition is highly operator-dependent, where rapid probe motion and brightness fluctuations often lead to reconstruction errors that reduce trust and clinical utility. We present UltrasODM, a dual-stream framework that assists sonographers during acquisition through calibrated per-frame uncertainty, saliency-based diagnostics, and actionable prompts. UltrasODM integrates (i) a contrastive ranking module that groups frames by motion similarity, (ii) an optical-flow stream fused with Dual-Mamba temporal modules for robust 6-DoF pose estimation, and (iii) a Human-in-the-Loop (HITL) layer combining Bayesian uncertainty, clinician-calibrated thresholds, and saliency maps highlighting regions of low confidence. When uncertainty exceeds the threshold, the system issues unobtrusive alerts suggesting corrective actions such as re-scanning highlighted regions or slowing the sweep. Evaluated on a clinical freehand ultrasound dataset, UltrasODM reduces drift by 15.2%, distance error by 12.1%, and Hausdorff distance by 10.1% relative to UltrasOM, while producing per-frame uncertainty and saliency outputs. By emphasizing transparency and clinician feedback, UltrasODM improves reconstruction reliability and supports safer, more trustworthy clinical workflows. Our code is publicly available at this https URL.
Abstract (translated)
临床超声检查高度依赖于操作员，快速探头移动和亮度波动常常导致重建错误，从而降低信任度和临床实用性。我们提出了一种名为UltrasODM的双流框架，该框架通过每帧校准不确定性和基于显著性图的诊断以及可执行提示来帮助超声技术人员进行检查。UltrasODM集成了(i)一个对比排名模块，按照运动相似性分组图像帧；(ii)一个融合了Dual-Mamba时间模块的光流流，用于稳健的6自由度姿态估计；(iii)一个人机交互（Human-in-the-Loop）层，结合了贝叶斯不确定性、临床医生校准的阈值和突出显示低置信区域的显著性图。当不确定性超过阈值时，系统会发出不显眼的警报，建议采取纠正措施，如重新扫描被标记的区域或减缓扫查速度。 在一项使用临床自由手超声数据集进行评估的研究中，UltrasODM比UltrasOM减少了15.2%的姿态漂移、12.1%的距离误差以及10.1%的豪斯多夫距离，并且还生成了每帧不确定性及显著性输出。通过强调透明度和临床医生反馈，UltrasODM提高了重建可靠性并支持更安全、更可信的临床工作流程。 我们的代码在以下链接公开提供：[此处应填写具体的URL]。
URL
https://arxiv.org/abs/2512.07756
