针对胎儿超声关键部位重建，提出分阶段聚焦优化方法提升临床可用性。
Focus on What Matters: Two-Stage ROI-Aware Refinement for Anatomy-Preserving Fetal Ultrasound Reconstruction
- 先全局重建再聚焦关键区域，用边缘和强度约束精细优化。
- 在保留医院数据上，关键区域误差降低6.4%~11.1%，整体质量提升0.29dB。
- 方法通用性强，适用于其他胎儿测量目标，适合医疗影像小区域决策场景。
测量关键的超声任务常依赖于小范围解剖结构，全局重建指标难以反映临床真实质量。本文提出一种面向感兴趣区域（ROI）的表征学习框架，并应用于多医院域偏移下的早孕期颈项透明层（NT）筛查。采用两阶段卷积自编码器：第一阶段通过MS-SSIM学习128维全局忠实的隐向量；第二阶段利用强度（L1）与归一化Sobel边缘约束，对NT ROI进行精细化重构。为融合异构目标而无需人工调参，基于各损失项梯度幅值进行梯度驱动的初始权重校准。在严格医院独占评估下，该方法同时提升全局与测量相关质量：标准开发集上PSNR分别提升+0.27 dB（验证集）与+0.29 dB（保留测试集），关键区域MAE降低8.87%（验证集）与6.43%（保留测试集），边缘误差降低11.10%（源医院）与4.90%（未见医院）。此外，冻结隐向量的探针分析显示泛化能力增强：未见医院上的医院来源可预测性下降（最大softmax从0.556降至0.541，熵从0.684升至0.688），而跨站异常检测性能保持强劲（马氏距离AUROC最高达0.9956，少数情形下KNN有小幅增益）。该ROI聚焦优化原则具有解剖无关性，可推广至其他胎儿生物测量目标（如头臀长CRL、鼻骨NB）及临床决策依赖小区域的医学影像场景。
原文摘要 · Abstract (English)
Measurement-critical ultrasound tasks often depend on a small anatomical region, making global reconstruction metrics an unreliable proxy for clinical fidelity. We propose an ROI-aware representation learning framework and instantiate it for first-trimester nuchal translucency (NT) screening under multi-hospital domain shift. A two-phase convolutional autoencoder (CAE) first learns a globally faithful 128-D latent code via MS-SSIM, then refines the NT ROI using intensity (L1) and normalized Sobel-edge constraints. To combine these heterogeneous objectives without manual tuning, we initialize loss weights via gradient-based calibration from per-term gradient magnitudes. Under strict hospital-wise evaluation with one hospital held out, ROI refinement improves both global and measurement-relevant quality: on the standard dev split it increases PSNR by +0.27 dB (val) and +0.29 dB (held-out test), reduces ROI MAE by 8.87% (val) and 6.43% (held-out test), and reduces ROI Edge-MAE by 11.10% on source hospitals and 4.90% on the unseen hospital. Beyond reconstruction, frozen-latent probes provide additional evidence of generalization: hospital provenance becomes less confidently predictable on the unseen site (0.556 to 0.541 max-softmax; 0.684 to 0.688 entropy) while OOD detection remains strong across site-held-out protocols (Mahalanobis AUROC up to 0.9956, with modest KNN gains in challenging splits). The same ROI-aware refinement principle is anatomy-agnostic and can be adopted for other fetal biometry targets (e.g., crown-rump length (CRL), nasal bone (NB)) and broader medical imaging settings where small ROIs dominate clinical decisions.
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