用深度学习自动分割脊神经根，支持多种MRI扫描类型。
Segmentation of spinal rootlets across MRI contrasts with RootletSeg
- 基于多模态MRI数据训练深度模型，跨不同成像对比度实现根丝分割。
- 在4种扫描类型上平均Dice得分达0.64，最高0.67，分割精度良好。
- 开源工具，适用于神经病变分析、神经调控等临床研究场景。
目的：开发一种深度学习方法，实现不同MRI扫描中脊神经根丝的自动分割。材料与方法：本回顾性研究纳入两个公开数据集和一个私有数据集的MRI扫描，包含3D各向同性3T TSE T2加权（T2w）及7T MP2RAGE（T1w-INV1、INV2，UNIT1）扫描。构建深度学习模型RootletSeg，用于分割C2-T1节段背侧与腹侧脊神经根丝。训练使用76例扫描，测试17例。采用Dice系数比较模型性能，并与现有开源方法对比；通过Bland-Altman分析将模型分割所得脊柱节段与椎间盘定义的椎体节段进行一致性评估。结果：基于93例来自50名健康成年人（平均年龄28.70岁±6.53，男性28人[56%]，女性22人[44%]）的MRI数据，RootletSeg在四种对比度上的平均Dice分数分别为：T1w-INV2为0.67±0.09，UNIT1为0.65±0.11，T2w为0.64±0.08，T1w-INV1为0.62±0.10。脊柱-椎体节段对应显示渐进性头尾移位，Bland-Altman偏倚范围为0.00至8.15毫米（节段中点差值中位数）。结论：RootletSeg能准确分割跨多种MRI对比度的C2-T1脊神经根丝，可直接从MRI确定脊柱节段，具有开源性，适用于病变分类、神经调控治疗及功能磁共振群组分析等下游任务。
原文摘要 · Abstract (English)
Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Methods: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed to segment C2-T1 dorsal and ventral spinal rootlets. Training was performed on 76 scans and testing on 17 scans. The Dice score was used to compare the model performance with an existing open-source method. Spinal levels derived from RootletSeg segmentations were compared with vertebral levels defined by intervertebral discs using Bland-Altman analysis. Results: The RootletSeg model developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years $\pm$ 6.53 [SD]; 28 [56%] males, 22 [44%] females) achieved a mean $\pm$ SD Dice score of 0.67 $\pm$ 0.09 for T1w-INV2, 0.65 $\pm$ 0.11 for UNIT1, 0.64 $\pm$ 0.08 for T2w, and 0.62 $\pm$ 0.10 for T1w-INV1 contrasts. Spinal-vertebral level correspondence showed a progressively increasing rostrocaudal shift, with Bland-Altman bias ranging from 0.00 to 8.15 mm (median difference between level midpoints). Conclusion: RootletSeg accurately segmented C2-T1 spinal rootlets across MRI contrasts, enabling the determination of spinal levels directly from MRI scans. The method is open-source and can be used for a variety of downstream analyses, including lesion classification, neuromodulation therapy, and functional MRI group analysis.
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