Tu Liyun, Porras Antonio R, Oh Albert, Lepore Natasha, Buck Graham C, Tsering Deki, Enquobahrie Andinet, Keating Robert, Rogers Gary F, Linguraru Marius George
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Health System, Washington DC, USA.
Division of Plastic and Reconstructive Surgery, Children's National Health System, Washington DC, USA.
Proc SPIE Int Soc Opt Eng. 2019 Feb;10950. doi: 10.1117/12.2512272. Epub 2019 Mar 13.
The evaluation of head malformations plays an essential role in the early diagnosis, the decision to perform surgery and the assessment of the surgical outcome of patients with craniosynostosis. Clinicians rely on two metrics to evaluate the head shape: head circumference (HC) and cephalic index (CI). However, they present a high inter-observer variability and they do not take into account the location of the head abnormalities. In this study, we present an automated framework to objectively quantify the head malformations, HC, and CI from three-dimensional (3D) photography, a radiation-free, fast and non-invasive imaging modality. Our method automatically extracts the head shape using a set of landmarks identified by registering the head surface of a patient to a reference template in which the position of the landmarks is known. Then, we quantify head malformations as the local distances between the patient's head and its closest normal from a normative statistical head shape multi-atlas. We calculated cranial malformations, HC, and CI for 28 patients with craniosynostosis, and we compared them with those computed from the normative population. Malformation differences between the two populations were statistically significant (p<0.05) at the head regions with abnormal development due to suture fusion. We also trained a support vector machine classifier using the malformations calculated and we obtained an improved accuracy of 91.03% in the detection of craniosynostosis, compared to 78.21% obtained with HC or CI. This method has the potential to assist in the longitudinal evaluation of cranial malformations after surgical treatment of craniosynostosis.
头部畸形的评估在颅缝早闭患者的早期诊断、手术决策以及手术效果评估中起着至关重要的作用。临床医生依靠两个指标来评估头部形状：头围（HC）和头指数（CI）。然而，它们存在较高的观察者间变异性，并且没有考虑头部异常的位置。在本研究中，我们提出了一个自动化框架，用于从三维（3D）摄影中客观量化头部畸形、头围和头指数，三维摄影是一种无辐射、快速且非侵入性的成像方式。我们的方法通过将患者的头部表面与地标位置已知的参考模板进行配准，使用一组地标自动提取头部形状。然后，我们将头部畸形量化为患者头部与其从规范统计头部形状多图谱中最接近的正常形状之间的局部距离。我们计算了28例颅缝早闭患者的颅骨畸形、头围和头指数，并将它们与从正常人群中计算得到的结果进行比较。在因缝合融合而发育异常的头部区域，两组人群之间的畸形差异具有统计学意义（p<0.05）。我们还使用计算得到的畸形训练了一个支持向量机分类器，在颅缝早闭的检测中，我们获得了91.03%的改进准确率，相比之下，使用头围或头指数获得的准确率为78.21%。该方法有可能有助于颅缝早闭手术治疗后颅骨畸形的纵向评估。
