Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction
Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction
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DOI：
10.1016/j.ejrad.2021.109817
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发表时间：
2021-06-16
影响因子：
3.3
通讯作者：
Wech, Tobias
中科院分区：
文献类型：
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作者：
Heidenreich, Julius F.;Gassenmaier, Tobias;Wech, Tobias
目的：从心肌梗死患者的晚期钆增强（LGE）心脏MR （CMR）中全自动获得定量参数，并研究相敏或幅度重建或两者结合是否能获得最佳分割精度。方法：在这项回顾性单中心研究中，我们训练了一个具有U-Net结构和自配置框架的卷积神经网络（“nnU-net”），用于LGE-CMR左心室心肌和梗死区分割。我们收集了78例心肌梗死病史患者的170项检查数据。采用相位敏感反演恢复、幅度重建或两种对比度作为输入通道，分别对模型进行拟合。手工标签是最基本的事实。在10例患者的子集中，通过确定SOrensen-Dice相似系数（DSC）和梗死区体积，使用Pearson's r相关和Bland Altman分析，将训练模型的性能与人工基础真实值进行定量比较。结果：该模型心肌组织与瘢痕组织具有较高的相似系数。使用PSIR、幅度重建或两种对比作为输入（PSIR和MAG；心肌的平均DSC: 0.83 +/- 0.03，疤痕的平均DSC: 0.72 +/- 0.08）之间没有显著差异。在人工方法和基于模型的方法之间观察到梗死区体积有很强的相关性（r = 0.96），对神经网络获得的体积有明显的低估。结论：与人工分割相比，自配置nnU-net的预测结果具有很强的一致性，证明了作为一种有前途的工具提供全自动定量评估LGE-CMR的潜力。
Purpose: To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy.Methods: In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework ("nnU-net") was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels. Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the SOrensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson's r correlation and Bland Altman analysis.Results: The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 +/- 0.03 for myocardium and 0.72 +/- 0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network.Conclusion: The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR.