Department of Computer Science and Information Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan.
Department of Computer Science and Information Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan.
Comput Methods Programs Biomed. 2019 Apr;171:27-37. doi: 10.1016/j.cmpb.2019.02.006. Epub 2019 Feb 12.
The calcaneus is the most fracture-prone tarsal bone and injuries to the surrounding tissue are some of the most difficult to treat. Currently there is a lack of consensus on treatment or interpretation of computed tomography (CT) images for calcaneus fractures. This study proposes a novel computer-assisted method for automated classification and detection of fracture locations in calcaneus CT images using a deep learning algorithm.
Two types of Convolutional Neural Network (CNN) architectures with different network depths, a Residual network (ResNet) and a Visual geometry group (VGG), were evaluated and compared for the classification performance of CT scans into fracture and non-fracture categories based on coronal, sagittal, and transverse views. The bone fracture detection algorithm incorporated fracture area matching using the speeded-up robust features (SURF) method, Canny edge detection, and contour tracing.
Results showed that ResNet was comparable in accuracy (98%) to the VGG network for bone fracture classification but achieved better performance for involving a deeper neural network architecture. ResNet classification results were used as the input for detecting the location and type of bone fracture using SURF algorithm.
Results from real patient fracture data sets demonstrate the feasibility using deep CNN and SURF for computer-aided classification and detection of the location of calcaneus fractures in CT images.
跟骨是最易发生骨折的跗骨，周围组织的损伤是最难治疗的损伤之一。目前，跟骨骨折的治疗或 CT 图像解读尚无共识。本研究提出了一种新的计算机辅助方法，使用深度学习算法自动分类和检测跟骨 CT 图像中的骨折部位。
评估并比较了两种具有不同网络深度的卷积神经网络（CNN）结构，即残差网络（ResNet）和视觉几何组（VGG），根据冠状位、矢状位和横断面，基于 CT 扫描将骨折和非骨折类别进行分类的性能。骨骨折检测算法结合了使用加速稳健特征（SURF）方法、Canny 边缘检测和轮廓跟踪的骨折区域匹配。
结果表明，ResNet 在骨骨折分类的准确性（98%）方面与 VGG 网络相当，但涉及更深的神经网络结构时，性能更好。ResNet 分类结果被用作使用 SURF 算法检测骨骨折位置和类型的输入。
来自真实患者骨折数据集的结果证明了使用深度 CNN 和 SURF 进行 CT 图像中跟骨骨折的计算机辅助分类和检测位置的可行性。
