Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries from lumbar MR images. Deep neural network (DNN) models may assist clinicians with more efficient image segmentation of individual instances (disks and vertebrae) of the lumbar spine in an automated way, which is termed as instance image segmentation. In this work, we proposed SymTC, an innovative lumbar spine MR image segmentation model that combines the strengths of Transformer and Convolutional Neural Network (CNN). Specifically, we designed a parallel dual-path architecture to merge CNN layers and Transformer layers, and we integrated a novel position embedding into the self-attention module of Transformer, enhancing the utilization of positional information for more accurate segmentation. To further improves model performance, we introduced a new data augmentation technique to create synthetic yet realistic MR image dataset, named SSMSpine, which is made publicly available. We evaluated our SymTC and the other 15 existing image segmentation models on our private in-house dataset and the public SSMSpine dataset, using two metrics, Dice Similarity Coefficient and 95% Hausdorff Distance. The results show that our SymTC has the best performance for segmenting vertebral bones and intervertebral discs in lumbar spine MR images. The SymTC code and SSMSpine dataset are available at https://github.com/jiasongchen/SymTC.

翻译：椎间盘疾病作为一种常见病症，常引发间歇性或持续性腰痛，而该病的诊断与评估依赖于从腰椎MR图像中精确测量椎骨及椎间盘的几何结构。深度神经网络模型可通过自动化方式辅助临床医生更高效地完成腰椎个体实例（椎间盘与椎骨）的图像分割，此过程称为实例图像分割。本文提出了一种创新的腰椎MR图像分割模型SymTC，该模型融合了Transformer与卷积神经网络的优势。具体而言，我们设计了并行双路径架构以合并CNN层与Transformer层，并在Transformer的自注意力模块中集成了新型位置嵌入技术，从而增强位置信息利用率以实现更精确的分割。为进一步提升模型性能，我们引入了一种新的数据增强技术，构建了名为SSMSpine的合成但逼真的MR图像数据集，并已公开发布。我们在私有内部数据集与公开SSMSpine数据集上，采用Dice相似系数和95%豪斯多夫距离两项指标评估了SymTC及其他15种现有图像分割模型。结果表明，SymTC在腰椎MR图像中分割椎骨与椎间盘方面性能最优。SymTC代码和SSMSpine数据集已发布于https://github.com/jiasongchen/SymTC。