IEEE J Biomed Health Inform. 2021 Jul;25(7):2643-2654. doi: 10.1109/JBHI.2020.3035888. Epub 2021 Jul 27.
Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra-/inter-observer variability, and can mislead the diagnose. Thus, automatic methods for anthropometric landmark detection in 3D models have a high interest in clinical practice. In this paper, a novel framework is proposed to accurately detect landmarks in 3D infant's head surfaces. The proposed method is divided into two stages: (i) 2D representation of the 3D head surface; and (ii) landmark detection through a deep learning strategy. Moreover, a 3D data augmentation method to create shape models based on the expected head variability is proposed. The proposed framework was evaluated in synthetic and real datasets, achieving accurate detection results. Furthermore, the data augmentation strategy proved its added value, increasing the method's performance. Overall, the obtained results demonstrated the robustness of the proposed method and its potential to be used in clinical practice for head shape analysis.
三维头部表面的地标标记是临床实践中评估头部形状的一项重要且常规的任务，即分析颅骨畸形或生长演变。然而，手动标记仍然适用，因为它是一项繁琐且耗时的任务，非常容易受到观察者内/间的变异性的影响，并且可能会导致误诊。因此，在临床实践中，用于三维模型中人体测量地标检测的自动方法具有很高的兴趣。在本文中，提出了一种新的框架来准确地检测三维婴儿头部表面的地标。该方法分为两个阶段：（i）3D 头部表面的 2D 表示；（ii）通过深度学习策略进行地标检测。此外，还提出了一种基于预期头部可变性的 3D 数据增强方法来创建形状模型。该框架在合成和真实数据集上进行了评估，实现了准确的检测结果。此外，数据增强策略证明了其附加值，提高了该方法的性能。总的来说，所获得的结果证明了该方法的稳健性及其在临床实践中用于头部形状分析的潜力。
