Shape model of the fetal face between 24 and 34 gestational weeks from segmented 3D US

This fetal face dataset is constituted of:- 184 3D surfaces of segmented fetal faces from 3D ultrasound acquired between 24 and 34 gestational weeks. Meshes have been scaled to adjust for gestational age (GA): data_age_rescaled.zip- a spreadsheet reporting individual GA and diagnosed facial abnormality categories: Patient DB 2023.csv- a shape model, or more precisely the parameters of the Deformetrica's deterministic atlas estimated for the above data: shape model.zipComplete description of the data and methodology is presented in [1] and [2]. The main objective of this study is to study the normal changes and the most common abnormality of the fetal face morphology at this stage to assist perinatal clinical follow-up.We summarise here the main information.Participants were prospectively recruited from two clinical centres. Exclusion criteria were based on the absence of suitable image or follow-up data. Gestational age and a short categorisation of the diagnosed pathological facial abnormalities are listed in the attached spreadsheet, it includes, in particular: fetal growth restriction, facial cleft, chromosomal abnormality (T18, T21), etc.Segmentation results were obtained by manual refinement of semi-automatic atlas-based approach. Meshes were then exported, scaled by GA, and rigidly aligned. Surfaces are available as vtk PolyData files in `data_age_rescaled.zip`.Scaling, at gestational age t (in days), is given by the following growth model, estimated by least-square regression on our data and standardized to scale 1 at 29 weeks (203 days) of GA:1/s(t) = 1 + 4.0077e-3 (t-203) - 3.2520e-5 (t-203)**2.This model has been validated against standard available references and is useful to reduce the morphological variability to ease the shape analysis. It can easily be reverted using the provided GA in the spreadsheet.Shape model files (`shape model.zip`) are respectively: the template (average) shape, the control points and momenta parametrizing the individual deformations from this template, the hyper-parameter of the model including the deformation kernel width kwd=7.0mm. The Deformetrica's framework is openly available there https://gitlab.com/icm-institute/aramislab/deformetrica.EthicsThe study has been approved by local research ethic committees in both centres (see [1,2]) and data acquired following patient informed consent.This dataset only contains non-identifiable data. In particular, the images themselves, that potentially contain extra information (compared to the final reconstruction) that may lead to the identification of the participants, are not included.References1. Sivera, R., Clark, A.E., Dall’Asta, A. et al. Fetal face shape analysis from prenatal 3D ultrasound images. Sci Rep 14, 4411 (2024). https://doi.org/10.1038/s41598-023-50386-92. Clark, A.E. Prenatal facial and brain analysis from 3D ultrasound. PhD thesis, Imperial College London (2024).

本胎儿面部数据集由以下内容构成：- 184 张在妊娠 24 至 34 周期间获取的 3D 超声影像分割的胎儿面部 3D 表面，网格已根据妊娠年龄（GA）进行调整以适应：数据_age_rescaled.zip- 一份报告个体 GA 和诊断面部异常类别（患者数据库 2023.csv）的电子表格：- 一个形状模型，更确切地说，是为上述数据估计的 Deformetrica 确定性图谱的参数：形状模型.zip数据和方法的具体描述详见 [1] 和 [2]。本研究的主要目标是研究胎儿面部形态在此阶段的正常变化和最常见的异常，以协助围产期临床随访。以下为该信息的总结。参与者系前瞻性地从两个临床中心招募。排除标准基于缺乏合适的图像或随访数据。妊娠年龄和诊断面部病理异常的简要分类列于所附电子表格中，包括：胎儿生长受限、面部裂隙、染色体异常（T18、T21）等。分割结果通过半自动图谱方法的手动细化获得。随后，网格被导出、按 GA 缩放并刚性对齐。表面作为 vtk PolyData 文件提供，存放在 `data_age_rescaled.zip` 中。在妊娠年龄 t（以天为单位）的缩放由以下生长模型给出，该模型通过对我们的数据进行的平方最小二乘回归估计，并标准化为 GA 29 周时（203 天）的缩放值为 1：1/s(t) = 1 + 4.0077e-3 (t-203) - 3.2520e-5 (t-203)**2。此模型已与标准可用的参考文献进行了验证，并有助于减少形态学变异性，便于形状分析。它可以通过提供的 GA 在电子表格中轻松回退。形状模型文件（`shape model.zip`）分别包括：模板（平均）形状、控制点和动量，这些参数化了个别变形；模型超参数，包括变形核宽度 kwd=7.0mm。Deformetrica 框架在 https://gitlab.com/icm-institute/aramislab/deformetrica 上公开可用。伦理学本研究的伦理审查已获得两个中心当地研究伦理委员会的批准（见 [1,2]），且所获取的数据均遵循患者知情同意。本数据集仅包含不可识别数据。特别是，图像本身可能包含相对于最终重建的额外信息，可能导致参与者的识别，这些图像不包括在内。参考文献1. Sivera, R.，Clark, A.E.，Dall'Asta, A. 等. 从产前 3D 超声影像分析胎儿面部形状。Sci Rep 14，4411（2024）。https://doi.org/10.1038/s41598-023-50386-92. Clark, A.E. 产前面部和大脑分析。博士论文，伦敦帝国理工学院（2024）。