Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 +- 0.16 (mean +- standard deviation across rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation <= 1.41 %), as well as low inter-session variability (coefficient of variation <= 1.30 %) indicating stable predictions across different MRI vendors, sites, and sessions. The proposed methodology is open-source and readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.

翻译：精确识别脊髓神经根对于划定脊髓水平以研究脊髓功能活动具有重要意义。本研究旨在开发一种基于T2加权磁共振成像（MRI）扫描自动进行脊髓神经根语义分割的方法。利用两个开放获取MRI数据集的图像，采用主动学习方法训练三维多类卷积神经网络，对C2-C8背侧神经根进行分割。每个输出类别对应一个脊髓节段。该方法在训练中未见过的3T T2加权图像数据集上进行了测试，以评估不同站点、不同扫描时段和不同分辨率下的变异性。测试Dice得分为0.67±0.16（各根水平的均值±标准差），表明性能良好。该方法还表现出较低的跨厂商和跨站点变异性（变异系数≤1.41%），以及较低的跨扫描时段变异性（变异系数≤1.30%），说明在不同MRI厂商、站点和扫描时段下预测结果稳定。所提出的方法为开源，并已在Spinal Cord Toolbox（SCT）v6.2及以上版本中提供。