Background. Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software and hardware is an ongoing challenge. Methods. Datasets from 3 medical centers acquired at 3T (n = 150 subjects) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. Results. The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (p = n.s.) whereas it significantly outperformed on the external datasets (p < 0.005 for exD-1 and exD-2). Moreover, the number of image series with "failed" segmentation was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). Conclusions. The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

翻译：背景。心肌灌注MRI数据集的全自动分析能够对疑似缺血性心脏病患者的负荷/静息研究进行快速客观的报告。尽管训练数据有限且软硬件存在差异，开发能够分析多中心数据集的深度学习技术仍是一个持续的挑战。方法。本研究纳入了来自3个医疗中心、在3T磁共振上采集的数据集（n = 150名受试者）：一个内部数据集（inD；n = 95）和两个用于评估训练好的深度神经网络（DNN）模型对脉冲序列差异（exD-1）和扫描仪厂商差异（exD-2）鲁棒性的外部数据集（exDs；n = 55）。使用inD的一个子集（n = 85）来训练/验证一个用于分割的DNN模型池，所有模型均使用相同的时空U-Net架构和超参数，但具有不同的参数初始化。我们采用了一种时空滑动块分析方法，该方法能自动生成像素级的"不确定性图"作为分割过程的副产品。在我们的方法中，给定的测试案例由DNN池中的所有成员进行分割，并利用生成的不确定性图自动从解决方案池中选择"最佳"的一个。结果。所提出的DAUGS分析方法在内部数据集上的表现与既定方法相似（p = n.s.），而在外部数据集上则显著优于既定方法（exD-1和exD-2的p < 0.005）。此外，与既定方法相比，所提方法的"失败"分割图像序列数量显著更低（4.3% vs. 17.1%，p < 0.0005）。结论。所提出的DAUGS分析方法有潜力提高深度学习方法在分割多中心负荷灌注数据集时的鲁棒性，这些数据集在脉冲序列选择、采集地点或扫描仪厂商方面存在差异。