聚焦关键内容：面向解剖保护的胎儿超声重建双阶段ROI感知细化
Focus on What Matters: Two-Stage ROI-Aware Refinement for Anatomy-Preserving Fetal Ultrasound Reconstruction
AI总结：
本文提出一种ROI感知的表示学习框架，用于在多医院域迁移下提升胎儿颈项透明度重建质量，通过双阶段卷积自编码器提升局部区域精度，并在严格评估中提升PSNR和减少误差。
摘要：
测量关键的超声任务往往依赖于小的解剖区域，使全局重建指标成为临床真实性的不可靠代理。我们提出了一种ROI感知的表示学习框架，并将其应用于多医院域迁移下的早孕期颈项透明度（NT）筛查。一个两阶段卷积自编码器（CAE）首先通过MS-SSIM学习一个全局忠实的128维潜在代码，然后使用强度（L1）和归一化Sobel边缘约束细化NT ROI。为了结合这些异质目标而无需手动调整，我们通过梯度基于的校准初始化损失权重。在严格医院级评估中，ROI细化提升了全局和测量相关质量：在标准dev分割上，PSNR增加+0.27 dB（val）和+0.29 dB（held-out测试），ROI MAE减少8.87%（val）和6.43%（held-out测试），ROI Edge-MAE减少11.10%在源医院和4.90%在未见医院。除了重建外，冻结潜在探针还提供了额外的泛化证据：医院来源在未见地点变得不那么有信心可预测（0.556到0.541 max-softmax；0.684到0.688熵）而OOD检测在跨地点held-out协议中保持强劲（Mahalanobis AUROC高达0.9956，具有挑战性分割中的适度KNN增益）。相同的ROI感知细化原则是解剖无关的，可以用于其他胎儿生物测量目标（如冠状窝长度（CRL）、鼻骨（NB））和更广泛的医学影像设置，其中小ROI主导临床决策。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
