Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging.

IF 4.2 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Chiara Manini, Markus Hüllebrand, Lars Walczak, Sarah Nordmeyer, Lina Jarmatz, Titus Kuehne, Heiko Stern, Christian Meierhofer, Andreas Harloff, Jennifer Erley, Sebastian Kelle, Peter Bannas, Ralf Felix Trauzeddel, Jeanette Schulz-Menger, Anja Hennemuth
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引用次数: 0

Abstract

Background: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) 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 the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow CMR 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 CMR 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 three-dimensional U-net convolutional neural network (CNN) architecture with residual units was trained for time-resolved two-dimensional aortic cross-sectional segmentation. 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.

Conclusion: This study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow CMR, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.

训练数据组成对 4D 流磁共振成像中 CNN 主动脉横截面分割通用性的影响。
背景:时间分辨三维相位对比磁共振成像(4D 流磁共振成像)在评估心血管疾病方面发挥着重要作用。然而,手动或半自动分割四维血流数据中的主动脉血管边界会带来变异,并限制主动脉血流动力学可视化和定量血流相关参数计算的可重复性。本文通过开发自动分割模型,探索了深度学习改善 4D 流量 MRI 分割的潜力,并分析了训练数据对模型在不同部位、扫描仪供应商、序列和病理中的泛化的影响:研究对象包括 260 个 4D 流磁共振成像数据集,其中包括在不同医院接受检查的无主动脉病变的受试者、健康志愿者和主动脉瓣二尖瓣(BAV)患者。数据集被拆分开来,以便在具有不同特征表示(如病理、性别、年龄、扫描仪型号、供应商和场强)的子集中训练分割模型。针对 2D+t 主动脉横截面分割训练了带有残差单元的增强型 3D U-net 卷积神经网络(CNN)架构。使用 Dice 评分、豪斯多夫距离和平均对称面距离对测试数据、训练集未体现特征的数据集(特定模型)和整体评估集进行了模型性能评估。利用布兰德-阿尔特曼分析和类间相关性计算标准诊断流程参数,并与人工分割结果进行比较:结果:在训练数据集中,扫描仪供应商和磁场强度等技术因素对总体分割性能的影响最大。年龄比性别的影响更大。仅在 BAV 患者数据集上训练的模型在健康受试者数据集上表现良好,反之则不然:本研究强调了在 4D 流磁共振成像中训练广泛适用的 CNN 自动分割时考虑异构数据集的重要性,尤其关注纳入不同病理和数据采集的技术方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.90
自引率
12.50%
发文量
61
审稿时长
6-12 weeks
期刊介绍: Journal of Cardiovascular Magnetic Resonance (JCMR) publishes high-quality articles on all aspects of basic, translational and clinical research on the design, development, manufacture, and evaluation of cardiovascular magnetic resonance (CMR) methods applied to the cardiovascular system. Topical areas include, but are not limited to: New applications of magnetic resonance to improve the diagnostic strategies, risk stratification, characterization and management of diseases affecting the cardiovascular system. New methods to enhance or accelerate image acquisition and data analysis. Results of multicenter, or larger single-center studies that provide insight into the utility of CMR. Basic biological perceptions derived by CMR methods.
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