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MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis.
Computers in Biology and Medicine ( IF 7.0 ) Pub Date : 2020-03-24 , DOI: 10.1016/j.compbiomed.2020.103728 Ming Li 1 , Chengjia Wang 2 , Heye Zhang 3 , Guang Yang 4
Computers in Biology and Medicine ( IF 7.0 ) Pub Date : 2020-03-24 , DOI: 10.1016/j.compbiomed.2020.103728 Ming Li 1 , Chengjia Wang 2 , Heye Zhang 3 , Guang Yang 4
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Multiview based learning has generally returned dividends in performance because additional information can be extracted for the representation of the diversity of different views. The advantage of multiview based learning fits the purpose of segmenting cardiac anatomy from multiview echocardiography, which is a non-invasive, low-cost and low-risk imaging modality. Nevertheless, it is still challenging because of limited training data, a poor signal-to-noise ratio of the echocardiographic data, and large variances across views for a joint learning. In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle. To tackle these challenges, a multiview recurrent aggregation network (MV-RAN) has been developed for the echocardiographic sequences segmentation with the full cardiac cycle analysis. Experiments have been carried out on multicentre and multi-scanner clinical studies consisting of spatio-temporal (2D + t) datasets. Compared to other state-of-the-art deep learning based methods, the MV-RAN method has achieved significantly superior results (0.92 ± 0.04 Dice scores) for the segmentation of the left ventricle on the independent testing datasets. For the estimation of clinical indices, our MV-RAN method has also demonstrated great promise and will undoubtedly propel forward the understanding of pathophysiological processes, computer-aided diagnosis and personalised prognosis using echocardiography.
中文翻译：
MV-RAN：用于超声心动图序列分割和完整心动周期分析的Multiview递归聚集网络。
基于多视图的学习通常可以提高性能，因为可以提取其他信息来表示不同视图的多样性。基于多视图的学习的优点适合于从多视图超声心动图分割心脏解剖结构的目的，这是一种非侵入性，低成本和低风险的成像方式。然而，由于训练数据有限，超声心动图数据的信噪比差以及联合学习的各个视图之间存在较大差异，这仍然具有挑战性。此外，为了更好地解释病理生理过程，临床决策和预后，理想的情况下，应针对覆盖整个心动周期的数据进行此类心脏解剖分割和各种临床指标的定量分析。为了应对这些挑战，已经开发了用于全心动周期分析的超声心动图序列分割的多视图递归聚集网络（MV-RAN）。已对由时空（2D + t）数据集组成的多中心和多扫描器临床研究进行了实验。与其他基于深度学习的最新技术相比，MV-RAN方法在独立测试数据集上对左心室的分割取得了明显优越的结果（0.92±0.04 Dice分数）。对于临床指标的估计，我们的MV-RAN方法也显示出了巨大的希望，并且无疑将推动对超声心动图的病理生理过程，计算机辅助诊断和个性化预后的理解。
更新日期：2020-04-20
中文翻译：
MV-RAN：用于超声心动图序列分割和完整心动周期分析的Multiview递归聚集网络。
基于多视图的学习通常可以提高性能，因为可以提取其他信息来表示不同视图的多样性。基于多视图的学习的优点适合于从多视图超声心动图分割心脏解剖结构的目的，这是一种非侵入性，低成本和低风险的成像方式。然而，由于训练数据有限，超声心动图数据的信噪比差以及联合学习的各个视图之间存在较大差异，这仍然具有挑战性。此外，为了更好地解释病理生理过程，临床决策和预后，理想的情况下，应针对覆盖整个心动周期的数据进行此类心脏解剖分割和各种临床指标的定量分析。为了应对这些挑战，已经开发了用于全心动周期分析的超声心动图序列分割的多视图递归聚集网络（MV-RAN）。已对由时空（2D + t）数据集组成的多中心和多扫描器临床研究进行了实验。与其他基于深度学习的最新技术相比，MV-RAN方法在独立测试数据集上对左心室的分割取得了明显优越的结果（0.92±0.04 Dice分数）。对于临床指标的估计，我们的MV-RAN方法也显示出了巨大的希望，并且无疑将推动对超声心动图的病理生理过程，计算机辅助诊断和个性化预后的理解。
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