Department of Computer Science and Information Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan.
Department of Mechanical Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan.
Injury. 2021 Mar;52(3):616-624. doi: 10.1016/j.injury.2020.09.010. Epub 2020 Sep 16.
Classification of the type of calcaneal fracture on CT images is essential in driving treatment. However, human-based classification can be challenging due to anatomical complexities and CT image constraints. The use of computer-aided classification system in standard practice is additionally hindered by the availability of training images. The aims of this study is to 1) propose a deep learning network combined with data augmentation technique to classify calcaneal fractures on CT images into the Sanders system, and 2) assess the efficiency of such approach with differential training methods.
In this study, the Principle component analysis (PCA) network was selected for the deep learning neural network architecture for its superior performance. CT calcaneal images were processed through PCA filters, binary hashing, and a block-wise histogram. The Augmentor pipeline including rotation, distortion, and flips was applied to generate artificial calcaneus fractured images. Two types of training approaches and five data sample sizes were investigated to evaluate the performance of the proposed system with and without data augmentation.
Compared to the original performance, use of augmented images during training improved network performance accuracy by almost twofold in classifying Sanders fracture types for all dataset sizes. A fivefold increase in the number of augmented training images improved network classification accuracy by 35%. The proposed deep CNN model achieved 72% accuracy in classifying CT calcaneal images into the four Sanders categories when trained with sufficient augmented artificial images.
The proposed deep-learning algorithm coupled with data augmentation provides a feasible and efficient approach to the use of computer-aided system in assisting physicians in evaluating calcaneal fracture types.
在 CT 图像上对跟骨骨折进行分类对于指导治疗至关重要。然而，由于解剖结构复杂和 CT 图像的限制，基于人的分类可能具有挑战性。在标准实践中使用计算机辅助分类系统还受到训练图像可用性的限制。本研究的目的是 1）提出一种深度学习网络结合数据增强技术，将 CT 图像上的跟骨骨折分类为桑德斯系统，2）评估使用不同训练方法的这种方法的效率。
在这项研究中，选择主成分分析（PCA）网络作为深度学习神经网络架构，因为它具有卓越的性能。通过 PCA 滤波器、二进制哈希和分块直方图对 CT 跟骨图像进行处理。应用增强器管道包括旋转、变形和翻转，以生成人工跟骨骨折图像。研究了两种类型的训练方法和五种数据样本大小，以评估有无数据增强时提出的系统的性能。
与原始性能相比，在训练中使用增强图像几乎将网络性能的准确率提高了一倍，可以对所有数据集大小的桑德斯骨折类型进行分类。增加五倍的增强训练图像数量将网络分类准确率提高了 35%。当使用足够数量的增强人工图像进行训练时，所提出的深度 CNN 模型在将 CT 跟骨图像分类为四个桑德斯类别时达到了 72%的准确率。
所提出的深度学习算法结合数据增强为使用计算机辅助系统协助医生评估跟骨骨折类型提供了一种可行且有效的方法。
