Abstract：
BACKGROUND: Time-resolved, three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow MRI segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies.
METHODS: The study population consists of 260 4D flow MRI datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics such as pathology, gender, age, scanner model, vendor, and field strength. An enhanced 3D U-net convolutional neural network (CNN) architecture with residual units was trained for 2D+t aortic cross-sectional segmentation. The model performance was evaluated using Dice score, Hausdorff distance, and average symmetric surface distance on test data, datasets with characteristics not represented in the training set (model-specific), and an overall evaluation set. Standard diagnostic flow parameters were computed and compared with manual segmentation results using Bland-Altman analysis and interclass correlation.
RESULTS: The representation of technical factors such as scanner vendor and field strength in the training dataset had the strongest influence on the overall segmentation performance. Age had a greater impact than gender. Models solely trained on BAV patients\' datasets performed well on datasets of healthy subjects but not vice versa.
CONCLUSIONS: This study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow MRI, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.
METHODS: The study population consists of 260 4D flow MRI datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics such as pathology, gender, age, scanner model, vendor, and field strength. An enhanced 3D U-net convolutional neural network (CNN) architecture with residual units was trained for 2D+t aortic cross-sectional segmentation. The model performance was evaluated using Dice score, Hausdorff distance, and average symmetric surface distance on test data, datasets with characteristics not represented in the training set (model-specific), and an overall evaluation set. Standard diagnostic flow parameters were computed and compared with manual segmentation results using Bland-Altman analysis and interclass correlation.
RESULTS: The representation of technical factors such as scanner vendor and field strength in the training dataset had the strongest influence on the overall segmentation performance. Age had a greater impact than gender. Models solely trained on BAV patients\' datasets performed well on datasets of healthy subjects but not vice versa.
CONCLUSIONS: This study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow MRI, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.
摘要：
背景：时间解决，三维相位对比磁共振成像（4D流MRI）在评估心血管疾病中起着重要作用。然而,4D流量数据中主动脉血管边界的手动或半自动分割引入了主动脉血流动力学可视化和定量流量相关参数计算的变异性和再现性。本文探讨了深度学习通过开发用于自动分割的模型来改善4D流MRI分割的潜力，并分析了训练数据对跨不同站点的模型泛化的影响，扫描仪供应商，序列，和病态。
方法：研究人群由260个4D流MRI数据集组成，包括没有已知主动脉病理学的受试者，健康的志愿者,以及在不同医院检查的二叶主动脉瓣（BAV）患者。对数据集进行拆分，以训练具有病理等不同特征表示的子集上的分割模型，性别,年龄,扫描仪模型,供应商,和场强。具有残差单元的增强型3DU-net卷积神经网络(CNN)架构被训练用于2D+t主动脉横截面分割。使用Dice评分评估模型性能，Hausdorff距离,和测试数据上的平均对称表面距离，具有未在训练集中表示的特征的数据集(特定于模型的)，和一个整体评估集。使用Bland-Altman分析和类间相关性计算标准诊断流量参数并与手动分割结果进行比较。
结果：训练数据集中的扫描仪供应商和场强等技术因素的表示对整体分割性能的影响最大。年龄的影响大于性别。仅在BAV患者数据集上训练的模型在健康受试者的数据集上表现良好，但反之亦然。
结论：这项研究强调了考虑异构数据集对4D流MRI中广泛适用的自动CNN分割训练的重要性，特别关注数据采集的不同病理和技术方面。
方法：研究人群由260个4D流MRI数据集组成，包括没有已知主动脉病理学的受试者，健康的志愿者,以及在不同医院检查的二叶主动脉瓣（BAV）患者。对数据集进行拆分，以训练具有病理等不同特征表示的子集上的分割模型，性别,年龄,扫描仪模型,供应商,和场强。具有残差单元的增强型3DU-net卷积神经网络(CNN)架构被训练用于2D+t主动脉横截面分割。使用Dice评分评估模型性能，Hausdorff距离,和测试数据上的平均对称表面距离，具有未在训练集中表示的特征的数据集(特定于模型的)，和一个整体评估集。使用Bland-Altman分析和类间相关性计算标准诊断流量参数并与手动分割结果进行比较。
结果：训练数据集中的扫描仪供应商和场强等技术因素的表示对整体分割性能的影响最大。年龄的影响大于性别。仅在BAV患者数据集上训练的模型在健康受试者的数据集上表现良好，但反之亦然。
结论：这项研究强调了考虑异构数据集对4D流MRI中广泛适用的自动CNN分割训练的重要性，特别关注数据采集的不同病理和技术方面。
