三维胎儿超声图像的自动面部轴线标准化
Automatic facial axes standardization of 3D fetal ultrasound images
AI总结：
本文提出一种基于三个模块（特征提取、旋转平移回归、空间变换器）的AI网络，对三维胎儿超声图像自动标准化面部轴线，在1180例数据上显著降低观察者间旋转变异性，有助于颅面异常的早期评估。
摘要：
颅面异常提示早期发育障碍，通常与许多遗传综合征相关。早期诊断至关重要，但超声（US）检查常常无法识别这些特征。本研究提出了一种AI驱动的工具，用于辅助临床医生在三维超声中标准化胎儿面部轴线/平面，从而减少超声医师的工作量并促进面部评估。我们的网络由三个模块构成——特征提取器、旋转与平移回归以及空间变换器——处理三个正交的二维切片，以估计标准化三维超声中面部平面所需的变换。这些变换通过一个可微模块（空间变换器模块）应用于原始三维超声，生成标准化的三维超声以及相应的二维面部标准平面。所使用的数据集包含1180张在妊娠第20周至第35周之间采集的胎儿面部三维超声图像。结果表明，我们的网络显著降低了测试集中观察者间的旋转变异性，平均测地角差为14.12°±18.27°，欧几里得角误差为7.45°±14.88°。这些发现证明了该网络能够有效标准化面部轴线，这对于一致的胎儿面部评估至关重要。总之，所提出的网络显示出在临床环境中提高胎儿面部评估一致性和准确性的潜力，有助于颅面异常的早期评估。
英文摘要：
Craniofacial anomalies indicate early developmental disturbances and are usually linked to many genetic syndromes. Early diagnosis is critical, yet ultrasound (US) examinations often fail to identify these features. This study presents an AI-driven tool to assist clinicians in standardizing fetal facial axes/planes in 3D US, reducing sonographer workload and facilitating the facial evaluation. Our network, structured into three blocks-feature extractor, rotation and translation regression, and spatial transformer-processes three orthogonal 2D slices to estimate the necessary transformations for standardizing the facial planes in the 3D US. These transformations are applied to the original 3D US using a differentiable module (the spatial transformer block), yielding a standardized 3D US and the corresponding 2D facial standard planes. The dataset used consists of 1180 fetal facial 3D US images acquired between weeks 20 and 35 of gestation. Results show that our network considerably reduces inter-observer rotation variability in the test set, with a mean geodesic angle difference of 14.12$^{\circ}$ $\pm$ 18.27$^{\circ}$ and an Euclidean angle error of 7.45$^{\circ}$ $\pm$ 14.88$^{\circ}$. These findings demonstrate the network's ability to effectively standardize facial axes, crucial for consistent fetal facial assessments. In conclusion, the proposed network demonstrates potential for improving the consistency and accuracy of fetal facial assessments in clinical settings, facilitating early evaluation of craniofacial anomalies.
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。
