论文标题
无辐射评估和颅突的分类的统计形状模型
A statistical shape model for radiation-free assessment and classification of craniosynostosis
论文作者
论文摘要
颅面畸形的评估需要稀疏可用的患者数据。统计形状模型提供了现实且合成的数据，从而可以比较公共数据集上的现有方法。 我们建立了颅突变患者的首个公开统计3D头模型，也是第一个针对1.5岁以下婴儿的模型。我们进一步提出了基于形状模型的分类管道，以区分三种不同类别的颅突变和对照组进行摄影表面扫描。据我们所知，我们的研究在迄今为止针对颅突的分类研究和统计形状建模的分类研究中使用了最大的颅突变患者数据集。 我们证明我们的形状模型的性能类似于人头的其他统计形状模型。该模型的第一本特征模特表示颅骨突出特异性病理。关于自动分类颅神经质，我们的分类方法的准确度为97.8％，可与其他计算机断层扫描和立体观念图的其他最先进的方法相媲美。 我们公开可用的颅骨特异性统计形状模型可以在现实和合成数据上评估颅内突变的。我们进一步介绍了一种基于形状模型的最先进的分类方法，用于无辐射诊断颅内突变。
The assessment of craniofacial deformities requires patient data which is sparsely available. Statistical shape models provide realistic and synthetic data enabling comparisons of existing methods on a common dataset. We build the first publicly available statistical 3D head model of craniosynostosis patients and the first model focusing on infants younger than 1.5 years. We further present a shape-model-based classification pipeline to distinguish between three different classes of craniosynostosis and a control group on photogrammetric surface scans. To the best of our knowledge, our study uses the largest dataset of craniosynostosis patients in a classification study for craniosynostosis and statistical shape modeling to date. We demonstrate that our shape model performs similar to other statistical shape models of the human head. Craniosynostosis-specific pathologies are represented in the first eigenmodes of the model. Regarding the automatic classification of craniosynostis, our classification approach yields an accuracy of 97.8%, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Our publicly available, craniosynostosis-specific statistical shape model enables the assessment of craniosynostosis on realistic and synthetic data. We further present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis.
