用于磁共振神经成像的自动周围神经分割。

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Nedim Christoph Beste, Johann Jende, Moritz Kronlage, Felix Kurz, Sabine Heiland, Martin Bendszus, Hagen Meredig
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引用次数: 0

摘要

背景:磁共振神经成像(MRN)越来越多地被用作周围神经病的诊断工具。定量测量可提高 MRN 的解释能力,但需要进行神经分割,这既费时又容易出错,而且尚未成为临床常规。在这项研究中,我们应用神经网络自动分割周围神经:方法:通过 5 倍交叉验证(CV),对神经分割网络进行训练,以分割 35 名健康人左右上肢 MRN 扫描的坐骨神经及其近端分支,共获得 70 个训练实例。在 60 名健康人的单侧 MRN 扫描的独立测试集上对模型性能进行了评估:CV中的平均狄斯相似系数(DSC)为0.892(95%置信区间[CI]:0.888-0.897),平均雅卡德指数(JI)为0.806(95% CI:0.799-0.814),平均豪斯多夫距离(HD)为2.146(95% CI:2.184-2.208)。对于独立测试集,DSC 和 JI 较低,而 HD 较高,平均 DSC 为 0.789(95% CI:0.760-0.815),平均 JI 为 0.672(95% CI:0.642-0.699),平均 HD 为 2.118(95% CI:2.047-2.190):基于深度学习的分割模型在神经分割任务中表现良好。未来的工作将侧重于扩展训练数据,并将患有周围神经病的个体纳入训练,以实现高级周围神经疾病特征描述:这些结果将作为一个基线,在此基础上开发一个自动定量 MRN 特征分析框架,应用于 MRN 检查的常规读取:定量测量可增强 MRN 解释,但需要复杂且具有挑战性的神经分割。我们提出的基于深度学习的分割模型性能良好。我们的结果可作为临床自动定量 MRN 分段的基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automated peripheral nerve segmentation for MR-neurography.

Automated peripheral nerve segmentation for MR-neurography.

Background: Magnetic resonance neurography (MRN) is increasingly used as a diagnostic tool for peripheral neuropathies. Quantitative measures enhance MRN interpretation but require nerve segmentation which is time-consuming and error-prone and has not become clinical routine. In this study, we applied neural networks for the automated segmentation of peripheral nerves.

Methods: A neural segmentation network was trained to segment the sciatic nerve and its proximal branches on the MRN scans of the right and left upper leg of 35 healthy individuals, resulting in 70 training examples, via 5-fold cross-validation (CV). The model performance was evaluated on an independent test set of one-sided MRN scans of 60 healthy individuals.

Results: Mean Dice similarity coefficient (DSC) in CV was 0.892 (95% confidence interval [CI]: 0.888-0.897) with a mean Jaccard index (JI) of 0.806 (95% CI: 0.799-0.814) and mean Hausdorff distance (HD) of 2.146 (95% CI: 2.184-2.208). For the independent test set, DSC and JI were lower while HD was higher, with a mean DSC of 0.789 (95% CI: 0.760-0.815), mean JI of 0.672 (95% CI: 0.642-0.699), and mean HD of 2.118 (95% CI: 2.047-2.190).

Conclusion: The deep learning-based segmentation model showed a good performance for the task of nerve segmentation. Future work will focus on extending training data and including individuals with peripheral neuropathies in training to enable advanced peripheral nerve disease characterization.

Relevance statement: The results will serve as a baseline to build upon while developing an automated quantitative MRN feature analysis framework for application in routine reading of MRN examinations.

Key points: Quantitative measures enhance MRN interpretation, requiring complex and challenging nerve segmentation. We present a deep learning-based segmentation model with good performance. Our results may serve as a baseline for clinical automated quantitative MRN segmentation.

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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
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