Table_2_Using Deep Learning to Detect Spinal Cord Diseases on Thoracolumbar Magnetic Resonance Images of Dogs.docx

Deep Learning based Convolutional Neural Networks (CNNs) are the state-of-the-art machine learning technique with medical image data. They have the ability to process large amounts of data and learn image features directly from the raw data. Based on their training, these networks are ultimately able to classify unknown data and make predictions. Magnetic resonance imaging (MRI) is the imaging modality of choice for many spinal cord disorders. Proper interpretation requires time and expertise from radiologists, so there is great interest in using artificial intelligence to more quickly interpret and diagnose medical imaging data. In this study, a CNN was trained and tested using thoracolumbar MR images from 500 dogs. T1- and T2-weighted MR images in sagittal and transverse planes were used. The network was trained with unremarkable images as well as with images showing the following spinal cord pathologies: intervertebral disc extrusion (IVDE), intervertebral disc protrusion (IVDP), fibrocartilaginous embolism (FCE)/acute non-compressive nucleus pulposus extrusion (ANNPE), syringomyelia and neoplasia. 2,693 MR images from 375 dogs were used for network training. The network was tested using 7,695 MR images from 125 dogs. The network performed best in detecting IVDPs on sagittal T1-weighted images, with a sensitivity of 100% and specificity of 95.1%. The network also performed very well in detecting IVDEs, especially on sagittal T2-weighted images, with a sensitivity of 90.8% and specificity of 98.98%. The network detected FCEs and ANNPEs with a sensitivity of 62.22% and a specificity of 97.90% on sagittal T2-weighted images and with a sensitivity of 91% and a specificity of 90% on transverse T2-weighted images. In detecting neoplasms and syringomyelia, the CNN did not perform well because of insufficient training data or because the network had problems differentiating different hyperintensities on T2-weighted images and thus made incorrect predictions. This study has shown that it is possible to train a CNN in terms of recognizing and differentiating various spinal cord pathologies on canine MR images. CNNs therefore have great potential to act as a “second eye” for imagers in the future, providing a faster focus on the altered image area and thus increasing workflow in radiology.

基于深度学习的卷积神经网络（CNNs）是目前在医疗图像数据处理中处于领先地位的机器学习技术。它们具备处理大量数据并直接从原始数据中学习图像特征的能力。基于其训练，这些网络最终能够对未知数据进行分类并作出预测。磁共振成像（MRI）是众多脊髓疾病的首选成像方式。正确的解读需要放射科医生的时间和专业知识，因此，利用人工智能技术快速解读和诊断医学影像数据引起了极大的兴趣。在本研究中，通过使用500只狗的胸腰段MRI图像对CNN进行了训练和测试。研究使用了矢状位和横断位的T1-和T2加权MRI图像。网络训练中不仅使用了正常图像，还使用了以下脊髓病理图像：椎间盘突出（IVDE）、椎间盘膨出（IVDP）、纤维软骨栓塞（FCE）/急性非压缩性髓核突出（ANNPE）、空洞症和肿瘤。用于网络训练的MRI图像共有2,693张，来自375只狗。网络测试使用了来自125只狗的7,695张MRI图像。在网络性能方面，其在矢状位T1加权图像上检测IVDPs时表现最佳，灵敏度达到100%，特异性为95.1%。网络在检测IVDEs方面也表现出色，尤其是在矢状位T2加权图像上，灵敏度为90.8%，特异性为98.98%。网络在矢状位T2加权图像上检测FCEs和ANNPEs的灵敏度为62.22%，特异性为97.90%，在横断位T2加权图像上的灵敏度和特异性分别为91%和90%。在检测肿瘤和空洞症时，由于训练数据不足或网络在T2加权图像上区分不同高信号区域能力不足，导致CNN表现不佳，从而作出了错误的预测。本研究表明，在犬类MRI图像上训练CNN以识别和区分各种脊髓病理学是可行的。因此，CNN在将来有望成为影像诊断医师的“第二双眼睛”，能更快地聚焦于图像的异常区域，从而提高放射科的工作流程。