Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, United States.
Computer Science Department, San Francisco State University, San Francisco, CA, United States.
Med Image Anal. 2014 Jul;18(5):699-710. doi: 10.1016/j.media.2014.04.002. Epub 2014 Apr 15.
Down syndrome, the most common single cause of human birth defects, produces alterations in physical growth and mental retardation. If missed before birth, the early detection of Down syndrome is crucial for the management of patients and disease. However, the diagnostic accuracy for pediatricians prior to cytogenetic results is moderate and the access to specialists is limited in many social and low-economic areas. In this study, we propose a simple, non-invasive and automated framework for Down syndrome detection based on disease-specific facial patterns. Geometric and local texture features are extracted based on automatically detected anatomical landmarks to describe facial morphology and structure. To accurately locate the anatomical facial landmarks, a hierarchical constrained local model using independent component analysis (ICA) is proposed. We also introduce a data-driven ordering method for selecting dominant independent components in ICA. The hierarchical structure of the model increases the accuracy of landmark detection by fitting separate models to different groups. Then the most representative features are selected and we also demonstrate that they match clinical observations. Finally, a variety of classifiers are evaluated to discriminate between Down syndrome and healthy populations. The best performance achieved 0.967 accuracy and 0.956 F1 score using combined features and linear discriminant analysis. The method was also validated on a dataset with mixed genetic syndromes and high performance (0.970 accuracy and 0.930 F1 score) was also obtained. The promising results indicate that our method could assist in Down syndrome screening effectively in a simple, non-invasive way, and extensible to detection of other genetic syndromes.
唐氏综合征是人类出生缺陷最常见的单一原因，会导致身体生长和智力发育迟缓。如果在出生前未能发现，那么对唐氏综合征的早期检测对于患者和疾病的管理至关重要。然而，儿科医生在细胞遗传学结果之前的诊断准确性适中，并且在许多社会和低经济地区，获得专家的机会有限。在这项研究中，我们提出了一种基于疾病特异性面部模式的简单、非侵入性和自动化的唐氏综合征检测框架。基于自动检测到的解剖学标志，提取几何和局部纹理特征，以描述面部形态和结构。为了准确定位解剖学面部标志，我们提出了一种使用独立成分分析（ICA）的分层约束局部模型。我们还引入了一种数据驱动的排序方法，用于选择 ICA 中的主导独立成分。该模型的分层结构通过将单独的模型拟合到不同的组来提高地标检测的准确性。然后选择最具代表性的特征，并且我们还证明它们与临床观察相匹配。最后，评估了各种分类器以区分唐氏综合征和健康人群。使用组合特征和线性判别分析，最佳性能达到了 0.967 的准确率和 0.956 的 F1 分数。该方法还在具有混合遗传综合征的数据集上进行了验证，并且也获得了很高的性能（准确率为 0.970，F1 分数为 0.930）。有前景的结果表明，我们的方法可以以简单、非侵入性的方式有效地辅助唐氏综合征筛查，并且可以扩展到其他遗传综合征的检测。
