Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study
Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study
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DOI：
10.3174/ajnr.a6070
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发表时间：
2019-06-01
影响因子：
3.5
通讯作者：
He, S.
中科院分区：
文献类型：
--
作者：
Fan, G.;Liu, H.;He, S.
背景和目的：目标区域的3D重建（“安全”三角和Kambin三角）可能有助于经椎间孔硬膜外类固醇注射的可行性评估，特别是在L5/S1水平。然而，手动分割腰骶神经进行三维重建是耗时的。本研究的目的是探讨基于深度学习的腰骶神经在CT上分割的可行性以及安全三角和Kambin三角的重建。材料和方法：共50例脊柱CT使用Slicer 4.8手动标记腰骶神经和骨骼。培训/验证/测试的比例为32：8：10。采用三维U形网建立腰骶部结构自动分割模型SPINECT。计算Dice评分、像素准确度和交集与并集，以评估SPINECT的分割性能。结果：CT图像显示腰骶部骨和神经的分割成功。骨骼的平均像素精度为0.940，神经为0.918。骨和神经的平均交叉连接为0.897和0.827。骨的Dice评分为0.945，神经为0.905。手动分割图像和自动分割图像之间的量化Kambin三角形或安全三角形无显著差异（P>.05）。结论：基于深度学习的常规CT腰骶部结构（神经和骨骼）自动分割是可行的，基于SPINECT的安全三角形和Kambin三角形的3D重建也得到了验证。
BACKGROUND AND PURPOSE: 3D reconstruction of a targeted area (" safe" triangle and Kambin triangle) may benefit the viability assessment of transforaminal epidural steroid injection, especially at the L5/S1 level. However, manual segmentation of lumbosacral nerves for 3D reconstruction is time-consuming. The aim of this study was to investigate the feasibility of deep learning-based segmentation of lumbosacral nerves on CT and the reconstruction of the safe triangle and Kambin triangle.MATERIALS AND METHODS: A total of 50 cases of spinal CT were manually labeled for lumbosacral nerves and bones using Slicer 4.8. The ratio of training/validation/testing was 32: 8: 10. A 3D U-Net was adopted to build the model SPINECT for automatic segmentations of lumbosacral structures. The Dice score, pixel accuracy, and Intersection over Union were computed to assess the segmentation performance of SPINECT. The areas of Kambin and safe triangles were measured to validate the 3D reconstruction.RESULTS: The results revealed successful segmentation of lumbosacral bone and nerve on CT. The average pixel accuracy for bone was 0.940, and for nerve, 0.918. The average Intersection over Union for bone was 0.897 and for nerve, 0.827. The Dice score for bone was 0.945, and for nerve, it was 0.905. There were no significant differences in the quantified Kambin triangle or safe triangle between manually segmented images and automatically segmented images (P>.05).CONCLUSIONS: Deep learning-based automatic segmentation of lumbosacral structures (nerves and bone) on routine CT is feasible, and SPINECT-based 3D reconstruction of safe and Kambin triangles is also validated.