Torres Helena R, Oliveira Bruno, Morais Pedro, Fritze Anne, Rüdiger Mario, Fonseca Jaime C, Vilaça João L
2Ai - School of Technology, IPCA, Barcelos, Portugal; Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal.
2Ai - School of Technology, IPCA, Barcelos, Portugal; Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal.
J Biomed Inform. 2022 Aug;132:104121. doi: 10.1016/j.jbi.2022.104121. Epub 2022 Jun 22.
Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed evaluation of the head shape. Artificial intelligence (AI) methods, namely deep learning (DL), can be explored to develop fast and automatic approaches for shape evaluation. However, due to the clinical variability of patients' head anatomy, generalization of AI networks to the clinical needs is paramount and extremely challenging. In this work, a new framework is proposed to augment the 3D data used for training DL networks for shape evaluation. The proposed augmentation strategy deforms head surfaces towards different deformities. For that, a point-based 3D morphable model (p3DMM) is developed to generate a statistical model representative of head shapes of different cranial deformities. Afterward, a constrained transformation approach (3DHT) is applied to warp a head surface towards a target deformity by estimating a dense motion field from a sparse one resulted from the p3DMM. Qualitative evaluation showed that the proposed method generates realistic head shapes indistinguishable from the real ones. Moreover, quantitative experiments demonstrated that DL networks training with the proposed augmented surfaces improves their performance in terms of head shape analysis. Overall, the introduced augmentation allows to effectively transform a given head surface towards different deformity shapes, potentiating the development of DL approaches for head shape analysis.
评估新生儿头部形状对于检测颅骨畸形、头部生长紊乱以及预测短期和长期神经发育情况很有必要。目前，缺乏能够提供详细头部形状评估的自动工具。可以探索人工智能（AI）方法，即深度学习（DL），来开发快速且自动的形状评估方法。然而，由于患者头部解剖结构的临床变异性，使人工智能网络适应临床需求的泛化至关重要且极具挑战性。在这项工作中，提出了一个新框架，以扩充用于训练形状评估深度学习网络的三维数据。所提出的扩充策略使头部表面朝着不同的畸形方向变形。为此，开发了一种基于点的三维可变形模型（p3DMM），以生成代表不同颅骨畸形头部形状的统计模型。之后，应用一种约束变换方法（3DHT），通过从p3DMM产生的稀疏运动场估计密集运动场，将头部表面朝着目标畸形方向扭曲。定性评估表明，所提出的方法生成的逼真头部形状与真实形状难以区分。此外，定量实验表明，使用所提出的扩充表面训练的深度学习网络在头部形状分析方面提高了其性能。总体而言，引入的扩充允许有效地将给定的头部表面朝着不同的畸形形状进行变换，促进了用于头部形状分析的深度学习方法的发展。
