Background: Automated segmentation of spinal MR images plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures presents challenges. Methods: This retrospective study, approved by the ethical committee, involved translating T1w and T2w MR image series into CT images in a total of n=263 pairs of CT/MR series. Landmark-based registration was performed to align image pairs. We compared 2D paired (Pix2Pix, denoising diffusion implicit models (DDIM) image mode, DDIM noise mode) and unpaired (contrastive unpaired translation, SynDiff) image-to-image translation using "peak signal to noise ratio" (PSNR) as quality measure. A publicly available segmentation network segmented the synthesized CT datasets, and Dice scores were evaluated on in-house test sets and the "MRSpineSeg Challenge" volumes. The 2D findings were extended to 3D Pix2Pix and DDIM. Results: 2D paired methods and SynDiff exhibited similar translation performance and Dice scores on paired data. DDIM image mode achieved the highest image quality. SynDiff, Pix2Pix, and DDIM image mode demonstrated similar Dice scores (0.77). For craniocaudal axis rotations, at least two landmarks per vertebra were required for registration. The 3D translation outperformed the 2D approach, resulting in improved Dice scores (0.80) and anatomically accurate segmentations in a higher resolution than the original MR image. Conclusion: Two landmarks per vertebra registration enabled paired image-to-image translation from MR to CT and outperformed all unpaired approaches. The 3D techniques provided anatomically correct segmentations, avoiding underprediction of small structures like the spinous process.

翻译：背景：脊柱MR图像的自动分割在科学和临床领域都起着至关重要的作用。然而，准确勾勒后部脊柱结构仍存在挑战。方法：这项经伦理委员会批准的回顾性研究，将T1w和T2w MR图像序列转换为CT图像，共涉及n=263对CT/MR序列。采用基于标志点的配准方法对齐图像对。我们比较了2D配对方法（Pix2Pix、去噪扩散隐式模型（DDIM）图像模式、DDIM噪声模式）和非配对方法（对比非配对翻译、SynDiff）的图像到图像翻译效果，以“峰值信噪比”（PSNR）作为质量衡量指标。利用公开可用的分割网络对合成CT数据集进行分割，并在内部测试集和“MRSpineSeg Challenge”数据集上评估Dice得分。将2D研究结果拓展至3D Pix2Pix和DDIM。结果：2D配对方法和SynDiff在配对数据上表现出相似的翻译效果和Dice得分。DDIM图像模式获得了最高的图像质量。SynDiff、Pix2Pix和DDIM图像模式的Dice得分相近（0.77）。在头尾轴旋转方面，每个椎骨至少需要两个标志点进行配准。3D翻译优于2D方法，使得Dice得分提升（0.80），且与原始MR图像相比，能以更高分辨率获得解剖结构准确的分割结果。结论：每个椎骨两个标志点的配准方法实现了从MR到CT的配对图像到图像翻译，且优于所有非配对方法。3D技术能够提供解剖结构正确的分割结果，避免了对棘突等小结构的欠预测。