用盲扫超声视频自动选关键帧，无操作者依赖地精准估胎重。
Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation
- 用视觉语言大模型选关键解剖帧，适配无约束盲扫场景。
- 在839例数据上误差仅161.3克，90.23%案例误差小于10%。
- 适合资源有限地区，无需专业操作即可完成胎重评估。
分娩前准确估算胎儿出生体重（FBW）对临床具有重要价值，但传统方法依赖操作者经验，尤其在资源匮乏地区难以推广。为降低对操作者的依赖，我们研究了在分娩前48小时内采集的盲扫超声（US）视频中进行近足月胎儿体重回归，以分娩后称重作为真实标签。为此，提出一种基于基础模型的关键解剖帧选择框架，可在无平面约束的盲扫视频中实现高精度的FBW回归。主要贡献包括：（1）首次使用盲扫超声视频估计胎儿体重，实现无操作者依赖的评估；（2）设计基于视觉-语言基础模型的解剖引导帧选择模块，用于在无约束扫描中提取关键帧；（3）提出冗余感知特征压缩模块，在保留任务相关性的同时缓解时间冗余。在839名患者前瞻性收集的数据上验证，本方法平均绝对误差（MAE）为161.3克，90.23%和100%的病例分别落在10%和15%绝对百分比误差范围内，优于典型哈德洛克估算及强竞争方法。代码已公开于https://github.com/ouleoule/BlindSweep-EBW。
原文摘要 · Abstract (English)
Accurate fetal birth weight (FBW) estimation shortly before delivery is clinically valuable yet challenging due to its reliance on operator expertise, particularly in low-resource settings. To reduce this reliance, we study near-term birth-weight regression from blind-sweep ultrasound (US) videos acquired within 48 hours prior to delivery, with post-delivery weighing as ground truth. Accordingly, we propose a foundation model-driven key anatomy frame selection framework that enables accurate FBW regression despite the absence of plane constraints in blind sweeps. Our highlights are as follows: (1) We believe this is the first work to estimate FBW using blind-sweep US videos, enabling operator-independent assessment. (2) An Anatomy-Guided Frame Selection module equipped with a vision-language foundation model is proposed for keyframe collection in unconstrained sweeps. (3) A Redundancy-Aware Feature Compression module is designed to compress frame features while preserving task-relevant information, alleviating temporal redundancy. Extensively validated on prospectively collected data from 839 patients, our method achieves an MAE of 161.3 g, with 90.23% and 100% of cases falling within 10% and 15% absolute percentage error, outperforming typical Hadlock estimation and strong competitors. Codes are available at https://github.com/ouleoule/BlindSweep-EBW.
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