Providing a simultaneous segmentation and diagnosis of the spinal structures on axial magnetic resonance imaging (MRI) images has significant value for subsequent pathological analyses and clinical treatments. However, this task remains challenging, owing to the significant structural diversity, subtle differences between normal and abnormal structures, implicit borders, and insufficient training data. In this study, we propose an innovative network framework called ‘Axial-SpineGAN’ comprising a generator, discriminator, and diagnostor, aiming to address the above challenges, and to achieve simultaneous segmentation and disease diagnosis for discs, neural foramens, thecal sacs, and posterior arches on axial MRI images. The generator employs an enhancing feature fusion module to generate discriminative features, i.e. to address the challenges regarding the significant structural diversity and subtle differences between normal and abnormal structures. An enhancing border alignment module is employed to obtain an accurate pixel classification of the implicit borders. The discriminator employs an adversarial learning module to effectively strengthen the higher-order spatial consistency, and to avoid overfitting owing to insufficient training data. The diagnostor employs an automated diagnosis module to provide automated recognition of spinal diseases. Extensive experiments demonstrate that these modules have positive effects on improving the segmentation and diagnosis accuracies. Additionally, the results indicate that Axial-SpineGAN has the highest Dice similarity coefficient (94.9%1.8%) in terms of the segmentation accuracy and highest accuracy rate (93.9%2.6%) in terms of the diagnosis accuracy, thereby outperforming existing state-of-the-art methods. Therefore, our proposed Axial-SpineGAN is effective and potential as a clinical tool for providing an automated segmentation and disease diagnosis for multiple spinal structures on MRI images.

在轴向磁共振成像 (MRI) 图像上提供脊柱结构的同时分割和诊断对于随后的病理分析和临床治疗具有重要价值。然而，由于显着的结构多样性、正常和异常结构之间的细微差异、隐含的边界以及训练数据不足，这项任务仍然具有挑战性。在这项研究中，我们提出了一种称为“Axial-SpineGAN”的创新网络框架，包括生成器、鉴别器和诊断器，旨在解决上述挑战，并实现椎间盘、神经孔、膜囊和膜囊的同步分割和疾病诊断。轴向 MRI 图像上的后弓。生成器采用增强特征融合模块来生成判别性特征，即 解决有关正常和异常结构之间显着结构多样性和细微差异的挑战。采用增强边界对齐模块来获得隐式边界的准确像素分类。判别器采用对抗性学习模块来有效加强高阶空间一致性，并避免由于训练数据不足而导致的过度拟合。诊断仪采用自动诊断模块来提供脊柱疾病的自动识别。大量的实验表明，这些模块对提高分割和诊断的准确性有积极的影响。此外，结果表明 Axial-SpineGAN 在分割准确率和准确率最高（93.9%2.5）方面具有最高的 Dice 相似系数（94.9%1.8%）。6%) 在诊断准确性方面，从而优于现有的最先进方法。因此，我们提出的 Axial-SpineGAN 是一种有效且有潜力的临床工具，可为 MRI 图像上的多个脊柱结构提供自动分割和疾病诊断。