专为胎儿超声帧选择设计，自动挑出高质量测量帧
FetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection
- 用冻结的视觉主干+混合多头结构，分任务筛选帧
- 在974帧测试集上达到0.956的平均AUROC和0.818相关性
- 适合临床超声自动化系统，尤其需精准测量的场景
胎儿生物测量的自动帧选择仍缺乏关注，多数先前工作仅针对通用质量评估或假设已有合适帧的下游测量流程。本文提出FetSelect，一种任务特定框架，结合冻结的视觉基础主干与混合多头设计：任务门控分类头与检测衍生质量头通过学习融合。我们收集了6,486张专家标注的帧，覆盖四个目标：头臀长（CRL）、颈项透明层（NT）、鼻骨（NB）和标尺。在19,019张未标注图像上使用BYOL进行自监督预训练。在保留测试集（974帧）上，FetSelect实现平均AUROC 0.956和与专家标注的相关性0.818。消融实验表明，混合融合优于单头变体，且超声特异性自监督带来稳定提升。在外部临床视频及509张外部CRL图像上的评估验证了其任务特异性判别能力。
原文摘要 · Abstract (English)
Automated frame selection for fetal biometry remains under addressed, with most prior work targeting generic quality assessment or downstream measurement pipelines that assume suitable frames are available. We introduce FetSelect, a task-specific framework that pairs a frozen vision foundation backbone with a hybrid multi-head design: a Task-Gated classification head and a Detection-derived quality head combined via learned fusion. We curate 6,486 expert-labeled frames across four targets: Crown-Rump Length (CRL), Nuchal Translucency (NT), Nasal Bone (NB), and Scalebar, and adapt the backbone with BYOL pretraining on 19,019 unlabeled images. On a held-out test set (974 frames), FetSelect achieves mean AUROC 0.956 and mean correlation 0.818 with expert quality annotations. Ablations confirm that hybrid fusion surpasses single-head variants, and ultrasound-specific self-supervision yields consistent gains. Evaluation on external clinical videos and 509 external CRL images demonstrates task-specific discrimination.
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