Kim Young Jae, Ganbold Bilegt, Kim Kwang Gi
Department of Biomedical Engineering, College of Health Science, Gachon University, Incheon, Korea.
Department of Biomedical Engineering, College of Medicine, Gachon Uinversity, Incheon, Korea.
Healthc Inform Res. 2020 Jan;26(1):61-67. doi: 10.4258/hir.2020.26.1.61. Epub 2020 Jan 31.
Back pain, especially lower back pain, is experienced in 60% to 80% of adults at some points during their lives. Various studies have found that lower back pain is a very common problem among adolescents, and the highest incidence rates are for adults in their 30s. There has been a remarkable increase in using computer-aided diagnosis to assist doctors in the interpretation of medical images. Spine segmentation in computed tomography (CT) scans using algorithmic methods allows improved diagnosis of back pain.
In this study, we developed a web-based automatic spine segmentation method using deep learning and obtained the dice coefficient by comparison with the predicted image. Our method is based on convolutional neural networks for segmentation. More specifically, we train a hierarchical data format file using U-Net architecture and then insert the test data label to perform segmentation. Thus, we obtained more specific and detailed results. A total of 344 CT images were used in the experiment. Of these, 330 were used for learning, and the remaining 14 for testing.
Our method achieved an average dice coefficient of 90.4%, a precision of 96.81%, and an F1-score of 91.64%.
The proposed web-based deep learning approach can be very practical and accurate for spine segmentation as a diagnostic method.
60%至80%的成年人在其生命中的某些时候会经历背痛，尤其是下背痛。各种研究发现，下背痛在青少年中是一个非常常见的问题，发病率最高的是30多岁的成年人。使用计算机辅助诊断来协助医生解读医学图像的情况显著增加。使用算法方法对计算机断层扫描（CT）图像进行脊柱分割有助于改善背痛的诊断。
在本研究中，我们开发了一种基于深度学习的基于网络的自动脊柱分割方法，并通过与预测图像比较获得了骰子系数。我们的方法基于用于分割的卷积神经网络。更具体地说，我们使用U-Net架构训练一个分层数据格式文件，然后插入测试数据标签以进行分割。因此，我们获得了更具体和详细的结果。实验共使用了344张CT图像。其中，330张用于学习，其余14张用于测试。
我们的方法平均骰子系数达到90.4%，精度为96.81%，F1分数为91.64%。
所提出的基于网络的深度学习方法作为一种诊断方法，在脊柱分割方面非常实用且准确。
