O' Sullivan Eimear, van de Lande Lara S, Oosting Anne-Jet C, Papaioannou Athanasios, Jeelani N Owase, Koudstaal Maarten J, Khonsari Roman H, Dunaway David J, Zafeiriou Stefanos, Schievano Silvia
Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
Department of Computing, Imperial College London, London, UK.
Bone Rep. 2021 Nov 29;15:101154. doi: 10.1016/j.bonr.2021.101154. eCollection 2021 Dec.
This study aims to capture the 3D shape of the human skull in a healthy paediatric population (0-4 years old) and construct a generative statistical shape model.
The skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population.
The 3DMM of the paediatric skull 0-4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases.
This study presents a reliable statistical shape model of the paediatric skull 0-4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field.
本研究旨在获取健康儿科人群（0至4岁）的人类颅骨三维形状，并构建一个生成性统计形状模型。
从计算机断层扫描（CT）图像重建了178名健康儿童（55%为男性，年龄20.8±12.9个月）的颅骨。在三维颅骨重建上放置了29个解剖标志点。去除旋转、平移和大小因素，使用无量纲颅骨网格模板和非刚性迭代最近点算法使所有颅骨网格紧密对应。使用主成分分析创建了一个三维可变形模型（3DMM），并通过人体测量进行内在和几何验证。利用3DMM生成合成颅骨实例，并通过将人体测量与选定的输入人群进行比较来验证。
成功构建了0至4岁小儿颅骨的3DMM。该模型相当紧凑——前10个主成分捕获了90%的模型形状方差。当使用所有模型成分时，量化3DMM表示训练期间未遇到的形状实例能力的泛化误差为0.47毫米。特异性值<0.7毫米，表明该模型生成的新颅骨实例是真实的。3DMM平均形状代表了选定人群（差异<2%）。总体而言，在从选定人群中提取的人体测量指标与规范文献数据（颞间距离最大差异）以及合成生成病例之间观察到了良好的一致性。
本研究提出了一个可靠的0至4岁小儿颅骨统计形状模型，该模型符合已知的颅骨形态测量指标，能够准确表示模型构建期间未使用的未见颅骨样本，并能生成新的真实颅骨实例，从而为该领域规范数据可用性有限的问题提供了一个解决方案。
