{"ID":5551760,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T10:26:15.620458842Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00745","arxiv_id":"2607.00745","title":"Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation","abstract":"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.","short_abstract":"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 hou...","url_abs":"https://arxiv.org/abs/2607.00745","url_pdf":"https://arxiv.org/pdf/2607.00745v1","authors":"[\"Le Ou\",\"Xiliang Zhu\",\"Huanwen Liang\",\"Wenxiong Pan\",\"Yuhao Huang\",\"Yuxiang Deng\",\"Xuan Sheng\",\"Hong Yin\",\"Juhua Xiao\",\"Xin Zhou\",\"Dong Ni\"]","published":"2026-07-01T10:27:16Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":613839,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-02T01:54:51.863792489Z","DeletedAt":null,"paper_id":5551760,"paper_url":"https://arxiv.org/abs/2607.00745","paper_title":"Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation","repo_url":"https://github.com/ouleoule/BlindSweep-EBW","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}