PDF(Pubmed)
Abstract：
Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising of 6707 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5 mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (> 0.988), with automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.
摘要：
自动密集3D表面配准是一种用于全面3D形状分析的强大技术，已在人体颅面形态学研究中成功应用，特别是在下颌和颅骨拱顶区域。然而,在探索人类头骨的正面时存在明显的差距，很大程度上是由于其颅骨解剖的复杂和独特的性质。为了更好地检查这个地区，这项研究介绍了一种简化的单表面颅面骨面罩，包括6707个准地标，这可以帮助分类和量化人类面部骨骼表面的变化。在通过锥形束计算机断层扫描（CBCT）成像获得的31个颅骨扫描数据集上进行了自动颅面骨表型鉴定。MeshMonk框架促进了构建的颅面骨面罩与每个单独的目标网格的非刚性对准。为了衡量这个自动化过程的准确性和可靠性，由三个独立的观察者在同一组图像上手动放置20个解剖面部标志三次。使用均方根（RMS）距离进行观察者内部和观察者之间的误差评估，显示出一贯的低分。随后，计算了相应的自动地标，并将其与手动放置的地标并列。这两个界标集之间的平均欧氏距离为1.5mm，而质心大小表现出值得注意的相似性。类内系数(ICC)表现出高水平的一致性(>0.988)，自动地标显示出较低的误差和变化。这些结果强调了这种新开发的单表面颅面骨面罩的实用性，结合MeshMonk框架，作为一种高度准确和可靠的方法，用于从CBCT和CT图像对人类头骨的面部区域进行自动表型分析。MeshMonk工具箱的这种颅面模板骨面罩扩展不仅增强了我们研究颅面骨变异的能力，而且还具有明显的遗传潜力，发展,以及人类颅面整体结构的进化基础。
