A major issue in translation of the artificial intelligence platforms for automatic segmentation of echocardiograms to clinics is their generalizability. The present study introduces and verifies a novel generalizable and efficient fully automatic multi-label segmentation method for four-chamber view echocardiograms based on deep fully convolutional networks (FCNs) and adversarial training. For the first time, we used generative adversarial networks for pixel classification training, a novel method in machine learning not currently used for cardiac imaging, to overcome the generalization problem. The method's performance was validated against manual segmentations as the ground-truth. Furthermore, to verify our method's generalizability in comparison with other existing techniques, we compared our method's performance with a state-of-the-art method on our dataset in addition to an independent dataset of 450 patients from the CAMUS (cardiac acquisitions for multi-structure ultrasound segmentation) challenge. On our test dataset, automatic segmentation of all four chambers achieved a dice metric of 92.1%, 86.3%, 89.6% and 91.4% for LV, RV, LA and RA, respectively. LV volumes' correlation between automatic and manual segmentation were 0.94 and 0.93 for end-diastolic volume and end-systolic volume, respectively. Excellent agreement with chambers’ reference contours and significant improvement over previous FCN-based methods suggest that generative adversarial networks for pixel classification training can effectively design generalizable fully automatic FCN-based networks for four-chamber segmentation of echocardiograms even with limited number of training data.

中文翻译：

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基于深度卷积对抗网络的四腔视图超声心动图的可推广全自动多标签分割

将用于自动分割超声心动图的人工智能平台转化为临床的一个主要问题是它们的普遍性。本研究介绍并验证了一种基于深度全卷积网络 (FCN) 和对抗性训练的四腔视图超声心动图的新型通用且高效的全自动多标签分割方法。我们第一次使用生成对抗网络进行像素分类训练，这是一种目前未用于心脏成像的机器学习新方法，以克服泛化问题。该方法的性能通过手动分割作为基本事实进行了验证。此外，为了验证我们的方法与其他现有技术相比的普遍性，我们比较了我们的方法 除了来自 CAMUS（用于多结构超声分割的心脏采集）挑战的 450 名患者的独立数据集之外，我们还使用最先进的方法在我们的数据集上提高了性能。在我们的测试数据集上，LV、RV、LA 和 RA 对所有四个腔室的自动分割分别实现了 92.1%、86.3%、89.6% 和 91.4% 的骰子度量。对于舒张末期容积和收缩末期容积，LV 容积在自动和手动分割之间的相关性分别为 0.94 和 0.93。