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：脊柱磁共振图像的自动分割在科学研究和临床实践中均具有重要作用。然而，精准勾画脊柱后部结构仍面临挑战。方法：本研究为回顾性研究，经伦理委员会批准，共纳入263对MRI/CT序列，将T1加权和T2加权MRI序列转换为CT图像。采用基于标志点的配准方法对齐图像对。以“峰值信噪比”（PSNR）为质量指标，比较了2D配对（Pix2Pix、去噪扩散隐式模型图像模式、去噪扩散隐式模型噪声模式）与非配对（对比非配对翻译、SynDiff）图像到图像转换方法。利用公开的分割网络对合成CT数据集进行分割，并通过内部测试集与“MRSpineSeg挑战赛”数据集评估Dice系数。2D研究结果被扩展至3D Pix2Pix和去噪扩散隐式模型。结果：在配对数据上，2D配对方法与SynDiff展现出相似的转换性能与Dice系数。去噪扩散隐式模型图像模式获得最高图像质量。SynDiff、Pix2Pix与去噪扩散隐式模型图像模式的Dice系数相近（0.77）。在头尾轴旋转中，每节椎骨至少需要两个标志点进行配准。3D转换优于2D方法，Dice系数提升至0.80，且分割结果在解剖结构精度和分辨率上均优于原始MRI图像。结论：每节椎骨两个标志点的配准方法实现了配对的MRI到CT图像转换，并优于所有非配对方法。3D技术可生成解剖结构正确的分割结果，避免棘突等小结构欠分割的问题。