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.

中文翻译：

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基于 RCNN 的颈髓损伤和椎间盘退变的更快检测。

磁共振成像（MRI）可以间接反映脊髓病变的微观变化；然而，深度学习在 MRI 中的应用来分类和检测颈脊髓疾病的病变尚未得到充分探索。在这项研究中，我们实现了 MRI 深度神经网络来检测颈椎疾病引起的病变。我们回顾性检查了 1,500 名患者的 MRI，无论他们是否患有颈椎疾病。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，并使用更快区域卷积神经网络（Faster R-CNN）与使用 ResNet-50 和 VGG-16 网络的主干卷积特征提取器相结合，来检测 MRI 期间的病变。实验结果表明，Faster R-CNN 结合 ResNet-50 和 VGG-16 在检测和识别颈髓 MRI 病变时的预测精度和速度令人满意。使用 ResNet-50 和 VGG-16 的 Faster R-CNN 的平均精度 (mAP) 分别为 88.6 和 72.3%，测试时间分别为 0.22 和 0.24 s/图像。Faster R-CNN 可以通过颈部 MRI 识别和检测病变。在某种程度上，它可能有助于放射科医生和脊柱外科医生的诊断。我们的研究结果可以为未来结合医学成像和深度学习的研究提供动力。