Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.
Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.
复制标题
使用 3D 摄影测量进行颅缝早闭手术结果评估的几何学习和统计模型。
DOI：
10.1016/j.cmpb.2023.107689
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发表时间：
2023
影响因子：
6.1
通讯作者：
Porras,AntonioR
中科院分区：
文献类型：
--
作者：
Elkhill,Connor;Liu,Jiawei;Linguraru,MariusGeorge;LeBeau,Scott;Khechoyan,David;French,Brooke;Porras,AntonioR
背景和目的颅面部标志的准确且可重复的检测对于头部发育异常的自动定量评估至关重要。由于儿科患者不鼓励使用传统成像方式，3D 摄影测量已成为评估颅面异常的流行且安全的成像替代方案。然而，传统的图像分析方法不适用于处理非结构化图像数据表示，例如 3D 摄影测量。方法我们提出了一种全自动流程来实时识别颅面标志，并使用它通过 3D 摄影测量来评估颅缝早闭患者的头部形状。为了检测颅面部标志，我们提出了一种基于切比雪夫多项式的新型几何卷积神经网络，以利用 3D 摄影测量中的点连接信息并量化多分辨率空间特征。我们提出了一种特定于地标的可训练方案，该方案聚合了在 3D 照片的每个顶点量化的多分辨率几何和纹理特征。然后，我们嵌入了一个新的概率距离回归器模块，该模块利用每个点的集成特征来预测地标位置，而无需假设与原始 3D 照片中的特定顶点的对应关系。最后，我们使用检测到的标志从颅缝早闭儿童的 3D 照片中分割颅骨，并得出一个新的头部形状异常统计指数来量化手术治疗后头部形状的改善。结果我们识别 Bookstein I 型颅面标志的平均误差为 2.74 ± 2.70 毫米，与其他最先进的方法相比，这是一个显着的改进。我们的实验还证明了 3D 照片中空间分辨率变化的高稳健性。最后，我们的头部形状异常指数量化了手术治疗导致的头部形状异常的显着减少。结论我们的全自动框架通过 3D 摄影测量提供实时颅面部标志检测，具有最先进的精度。此外，我们新的头部形状异常指数可以量化显着的头部表型变化，并可用于定量评估颅缝早闭患者的手术治疗。
Background and objectiveAccurate and repeatable detection of craniofacial landmarks is crucial for automated quantitative evaluation of head development anomalies. Since traditional imaging modalities are discouraged in pediatric patients, 3D photogrammetry has emerged as a popular and safe imaging alternative to evaluate craniofacial anomalies. However, traditional image analysis methods are not designed to operate on unstructured image data representations such as 3D photogrammetry.MethodsWe present a fully automated pipeline to identify craniofacial landmarks in real time, and we use it to assess the head shape of patients with craniosynostosis using 3D photogrammetry. To detect craniofacial landmarks, we propose a novel geometric convolutional neural network based on Chebyshev polynomials to exploit the point connectivity information in 3D photogrammetry and quantify multi-resolution spatial features. We propose a landmark-specific trainable scheme that aggregates the multi-resolution geometric and texture features quantified at every vertex of a 3D photogram. Then, we embed a new probabilistic distance regressor module that leverages the integrated features at every point to predict landmark locations without assuming correspondences with specific vertices in the original 3D photogram. Finally, we use the detected landmarks to segment the calvaria from the 3D photograms of children with craniosynostosis, and we derive a new statistical index of head shape anomaly to quantify head shape improvements after surgical treatment.ResultsWe achieved an average error of 2.74 ± 2.70 mm identifying Bookstein Type I craniofacial landmarks, which is a significant improvement compared to other state-of-the-art methods. Our experiments also demonstrated a high robustness to spatial resolution variability in the 3D photograms. Finally, our head shape anomaly index quantified a significant reduction of head shape anomalies as a consequence of surgical treatment.ConclusionOur fully automated framework provides real-time craniofacial landmark detection from 3D photogrammetry with state-of-the-art accuracy. In addition, our new head shape anomaly index can quantify significant head phenotype changes and can be used to quantitatively evaluate surgical treatment in patients with craniosynostosis.