Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
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DOI：
10.1186/s12968-018-0509-0
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发表时间：
2019-01-07
影响因子：
6.4
通讯作者：
Weinsaft, Jonathan W.
中科院分区：
文献类型：
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作者：
Bratt, Alex;Kim, Jiwon;Weinsaft, Jonathan W.
相位对比（PC）心血管磁共振（CMR）被广泛用于流量定量，但分析通常需要耗时的人工分割，这可能需要人工校正。机器学习的进步显著改善了自动化处理，但尚未应用于PC-CMR。本研究测试了一种新的机器学习模型，用于全自动分析PC-CMR主动脉流。方法设计基于神经网络的主动脉瓣边界跟踪机器学习模型。该模型在一个衍生队列中进行训练，该队列包括150名接受临床PC-CMR的患者，然后在前瞻性验证队列中与人工和商业上可用的自动分割进行比较。进一步的验证测试在从不同站点/CMR供应商获得的外部队列中进行。结果190例在商用扫描仪上进行CMR的冠状动脉疾病患者中（84%为1.5T， 16%为3T），机器学习分割一致成功，不需要人工干预：分割时间为
BackgroundPhase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow.MethodsA machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor.ResultsAmong 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was