用深度学习自动标准化胎儿超声面部切面，提升定位精度。
3DFETUS: Deep Learning-Based Standardization of Facial Planes in 3D Ultrasound
- 基于标注解剖点设计鲁棒算法，自动估计标准面部切面。
- 平均平移误差3.21±1.98mm，旋转误差5.31±3.945°，优于现有方法。
- 适用于产前胎儿面部评估，临床专家认可其准确性提升。
3D医学影像中解剖平面的自动定位与标准化仍面临物体姿态、外观及图像质量差异的挑战，尤其在胎儿超声中，散斑噪声和对比度不足进一步加剧难题。为解决胎儿面部评估中的此类问题，本文提出：1）GT++，一种利用标注解剖点估计标准面部切面的鲁棒算法；2）3DFETUS，一个深度学习模型，可自动化并标准化3D胎儿超声体积中的切面定位。通过专家临床评审与定量评估验证，该方法在3D US数据上实现每平面平均平移误差3.21±1.98mm、平均旋转误差5.31±3.945°，优于其他先进方法。临床评估进一步证实两者在切面估计精度上的显著提升。
原文摘要 · Abstract (English)
The automatic localization and standardization of anatomical planes in 3D medical imaging remains a challenging problem due to variability in object pose, appearance, and image quality. In 3D ultrasound, these challenges are exacerbated by speckle noise and limited contrast, particularly in fetal imaging. To address these challenges in the context of facial assessment, we present: 1) GT++, a robust algorithm that estimates standard facial planes from 3D US volumes using annotated anatomical landmarks; and 2) 3DFETUS, a deep learning model that automates and standardizes their localization in 3D fetal US volumes. We evaluated our methods both qualitatively, through expert clinical review, and quantitatively. The proposed approach achieved a mean translation error of 3.21 $\pm$ 1.98mm and a mean rotation error of 5.31 $\pm$ 3.945$^\circ$ per plane, outperforming other state-of-the-art methods on 3D US volumes. Clinical assessments further confirmed the effectiveness of both GT++ and 3DFETUS, demonstrating statistically significant improvements in plane estimation accuracy.
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