背景:腰椎管狭窄症(LSS)的诊断可能具有挑战性,因为在罪魁祸首水平定位中并不经常出现神经根疼痛。X线图像上腰椎硬膜的准确分割和定量分析是LSS准确鉴别诊断的关键。这项研究的目的是开发一种自动硬脑膜轮廓描绘工具,用于对LSS患者的计算机断层扫描脊髓造影(CTM)进行放射学定量。
方法:本研究共纳入518例伴或不伴腰椎管狭窄的CTM患者。部署了深度学习(DL)分割算法3维(3D)U-Net。总共使用了210个标记的病例来开发硬脑膜轮廓工具,随着训练的比例,独立测试,外部验证数据集为150:30:30。Dice评分(DCS)是评估3DU-Net分割性能的主要指标,随后被开发为硬脑膜轮廓工具,用于分割另一个未标记的308CTM病例。然后,人类专家对狭窄水平的446个切片的自动面罩进行了仔细的审查和修订,并比较硬脑膜的横截面积(CSA)。
结果:在五折交叉验证中,3DU-Net的平均DCS分别为0.905±0.080、0.933±0.018和0.928±0.034,独立测试,和外部验证数据集,分别。硬脑膜轮廓工具的分割性能也与第二观察者(人类专家)的分割性能相当。用硬脑膜轮廓工具,只有59.0%(263/446)的狭窄切片自动面罩需要修改。在修订后的案例中,自动口罩和相应修订口罩的硬脑膜CSA无显著差异(p=0.652).此外,在自动面罩和相应的修订面罩之间发现硬脑膜CSA的强相关性(r=0.805)。
结论:开发了一种硬脑膜成形工具,可以自动分割CTM上的硬脑膜囊,具有较高的准确性和泛化能力。此外,硬脑膜轮廓描绘工具有可能应用于LSS患者,因为它有助于量化狭窄切片上的硬脑膜CSA。
BACKGROUND: The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS.
METHODS: A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared.
RESULTS: The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805).
CONCLUSIONS: A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.