Ma Shaolong, Huang Yang, Che Xiangjiu, Gu Rui
Department of orthopedics, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China.
College of Computer Science and Technology, Jilin university, Changchun, China.
J Appl Clin Med Phys. 2020 Sep;21(9):235-243. doi: 10.1002/acm2.13001. Epub 2020 Aug 14.
Magnetic resonance imaging (MRI) can indirectly reflect microscopic changes in lesions on the spinal cord; however, the application of deep learning to MRI to classify and detect lesions for cervical spinal cord diseases has not been sufficiently explored. In this study, we implemented a deep neural network for MRI to detect lesions caused by cervical diseases. We retrospectively reviewed the MRI of 1,500 patients irrespective of whether they had cervical diseases. The patients were treated in our hospital from January 2013 to December 2018. We randomly divided the MRI data into three groups of datasets: disc group (800 datasets), injured group (200 datasets), and normal group (500 datasets). We designed the relevant parameters and used a faster-region convolutional neural network (Faster R-CNN) combined with a backbone convolutional feature extractor using the ResNet-50 and VGG-16 networks, to detect lesions during MRI. Experimental results showed that the prediction accuracy and speed of Faster R-CNN with ResNet-50 and VGG-16 in detecting and recognizing lesions from a cervical spinal cord MRI were satisfactory. The mean average precisions (mAPs) for Faster R-CNN with ResNet-50 and VGG-16 were 88.6 and 72.3%, respectively, and the testing times was 0.22 and 0.24 s/image, respectively. Faster R-CNN can identify and detect lesions from cervical MRIs. To some extent, it may aid radiologists and spine surgeons in their diagnoses. The results of our study can provide motivation for future research to combine medical imaging and deep learning.
磁共振成像（MRI）能够间接反映脊髓病变的微观变化；然而，深度学习在MRI用于颈椎脊髓疾病病变分类和检测方面的应用尚未得到充分探索。在本研究中，我们实现了一个用于MRI的深度神经网络来检测由颈椎疾病引起的病变。我们回顾性分析了1500例患者的MRI，无论他们是否患有颈椎疾病。这些患者于2013年1月至2018年12月在我院接受治疗。我们将MRI数据随机分为三组数据集：椎间盘组（800个数据集）、损伤组（200个数据集）和正常组（500个数据集）。我们设计了相关参数，并使用结合了基于ResNet - 50和VGG - 16网络的骨干卷积特征提取器的更快区域卷积神经网络（Faster R - CNN），在MRI过程中检测病变。实验结果表明，使用ResNet - 50和VGG - 16的Faster R - CNN在检测和识别颈椎脊髓MRI病变方面的预测准确性和速度令人满意。使用ResNet - 50和VGG - 16的Faster R - CNN的平均精度均值（mAP）分别为88.6%和72.3%，测试时间分别为0.22秒/图像和0.24秒/图像。Faster R - CNN能够从颈椎MRI中识别和检测病变。在一定程度上，它可能有助于放射科医生和脊柱外科医生进行诊断。我们的研究结果可为未来结合医学影像和深度学习的研究提供动力。
