Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Comput Methods Programs Biomed. 2022 Jun;221:106893. doi: 10.1016/j.cmpb.2022.106893. Epub 2022 May 19.
The fetal face is an essential source of information in the assessment of congenital malformations and neurological anomalies. Disturbance in early stages of development can lead to a wide range of effects, from subtle changes in facial and neurological features to characteristic facial shapes observed in craniofacial syndromes. Three-dimensional ultrasound (3D US) can provide more detailed information about the facial morphology of the fetus than the conventional 2D US, but its use for pre-natal diagnosis is challenging due to imaging noise, fetal movements, limited field-of-view, low soft-tissue contrast, and occlusions.
In this paper, we propose the use of a novel statistical morphable model of newborn faces, the BabyFM, for fetal face reconstruction from 3D US images. We test the feasibility of using newborn statistics to accurately reconstruct fetal faces by fitting the regularized morphable model to the noisy 3D US images.
The results indicate that the reconstructions are quite accurate in the central-face and less reliable in the lateral regions (mean point-to-surface error of 2.35 mm vs 4.86 mm). The algorithm is able to reconstruct the whole facial morphology of babies from US scans while handle adverse conditions (e.g. missing parts, noisy data).
The proposed algorithm has the potential to aid in-utero diagnosis for conditions that involve facial dysmorphology.
胎儿面部是评估先天性畸形和神经发育异常的重要信息来源。早期发育障碍可能导致广泛的影响，从面部和神经特征的细微变化到颅面综合征中观察到的特征性面部形状。三维超声（3D US）可提供比传统 2D US 更详细的胎儿面部形态信息，但由于成像噪声、胎儿运动、有限的视野、软组织对比度低和遮挡等原因，其在产前诊断中的应用具有挑战性。
在本文中，我们提出使用一种新的新生儿面部统计可变形模型（BabyFM），从 3D US 图像中重建胎儿面部。我们通过将正则化可变形模型拟合到有噪声的 3D US 图像，来测试使用新生儿统计数据准确重建胎儿面部的可行性。
结果表明，重建在中央面部非常准确，而在侧面区域则不太可靠（平均点到曲面误差为 2.35 毫米对 4.86 毫米）。该算法能够从 US 扫描中重建婴儿的整个面部形态，同时处理不良条件（例如缺失部分、噪声数据）。
所提出的算法有可能辅助涉及面部发育不良的宫内诊断。
