Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants ($\text{n}=267$; $\text{contrasts}=6$), we first generated participant-wise soft ground truth (GT) by averaging the binary segmentations across all 6 contrasts. These soft GT, along with a regression-based loss function, were then used to train a UNet model for spinal cord segmentation. We evaluated our model against state-of-the-art methods and performed ablation studies involving different GT mask types, loss functions, and contrast-specific models. Our results show that using the soft average segmentations along with a regression loss function reduces CSA variability ($p < 0.05$, Wilcoxon signed-rank test). The proposed spinal cord segmentation model generalizes better than the state-of-the-art contrast-specific methods amongst unseen datasets, vendors, contrasts, and pathologies (compression, lesions), while accounting for partial volume effects.

翻译：脊髓分割具有临床相关性，尤其用于计算脊髓横截面积（CSA）以诊断和监测脊髓压迫或多发性硬化等神经退行性疾病。尽管已有多种半自动和全自动方法，但一个关键限制依然存在：分割依赖于MRI对比度，导致不同对比度下的CSA结果存在差异。这一定程度上源于脊髓与脑脊液边界的影像表现随序列和采集参数而变化。这种对比度敏感的CSA在多中心研究（其中协议可能各不相同）中增加了变异性，降低了检测细微萎缩的灵敏度。此外，现有方法通过为每种对比度训练单独模型来加剧CSA变异性，同时生成无法考虑部分容积效应的二值掩膜。本研究提出一种基于深度学习的方法，可生成脊髓的软分割结果。利用健康被试的脊柱通用公共数据库（n=267；对比度种类=6），我们首先通过对所有6种对比度的二值分割结果取平均，生成被试级别的软标注真值。随后将软标注真值结合基于回归的损失函数，训练用于脊髓分割的UNet模型。我们评估了模型与最先进方法的性能，并开展了涉及不同真值掩膜类型、损失函数及对比度特异性模型的消融研究。结果表明，使用软平均分割结果结合回归损失函数可降低CSA变异性（p<0.05，Wilcoxon符号秩检验）。所提出的脊髓分割模型相较于最先进的对比度特异性方法，在未见数据集、厂商、对比度及病理情况（压迫、病灶）下泛化能力更优，同时能够处理部分容积效应。