PURPOSE The emerging market of cardiac handheld ultrasound (US) is on the rise. Despite the advantages in ease of access and the lower cost, a gap in image quality can still be observed between the echocardiography (echo) data captured by point-of-care ultrasound (POCUS) compared to conventional cart-based US, which limits the further adaptation of POCUS. In this work, we aim to present a machine learning solution based on recent advances in adversarial training to investigate the feasibility of translating POCUS echo images to the quality level of high-end cart-based US systems. METHODS We propose a constrained cycle-consistent generative adversarial architecture for unpaired translation of cardiac POCUS to cart-based US data. We impose a structured shape-wise regularization via a critic segmentation network to preserve the underlying shape of the heart during quality translation. The proposed deep transfer model is constrained to the anatomy of the left ventricle (LV) in apical two-chamber (AP2) echo views. RESULTS A total of 1089 echo studies from 841 patients are used in this study. The AP2 frames are captured by POCUS (Philips Lumify and Clarius) and cart-based (Philips iE33 and Vivid E9) US machines. The dataset of quality translation comprises a total of 441 echo studies from 395 patients. Data from both POCUS and cart-based systems of the same patient were available in 122 cases. The deep-quality transfer model is integrated into a pipeline for an automated cardiac evaluation task, namely segmentation of LV in AP2 view. By transferring the low-quality POCUS data to the cart-based US, a significant average improvement of 30% and 34 mm is obtained in the LV segmentation Dice score and Hausdorff distance metrics, respectively. CONCLUSION This paper presents the feasibility of a machine learning solution to transform the image quality of POCUS data to that of high-quality high-end cart-based systems. The experiments show that by leveraging the quality translation through the proposed constrained adversarial training, the accuracy of automatic segmentation with POCUS data could be improved.

中文翻译：

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使用受限制的CycleGAN，将心脏护理点转换为基于购物车的超声。

目的心脏手持超声（美国）的新兴市场正在上升。尽管具有易于访问和成本低廉的优势，但与常规的基于手推车的US相比，即时医疗超声（POCUS）捕获的超声心动图（echo）数据之间仍然观察到图像质量的差距，这限制了POCUS的进一步改编。在这项工作中，我们旨在基于对抗训练的最新进展，提供一种机器学习解决方案，以研究将POCUS回声图像转换为基于高端购物车的美国系统质量水平的可行性。方法我们提出了一种约束周期一致的生成对抗性架构，用于将心脏POCUS不成对翻译为基于购物车的US数据。我们通过评论者分割网络强加了结构化的形状规则化规则，以在质量转换过程中保留心脏的基本形状。提出的深度转移模型被限制在心尖两腔（AP2）回声视图中的左心室（LV）的解剖结构。结果本研究共使用了来自841名患者的1089项回声研究。AP2帧由POCUS（Philips Lumify和Clarius）和基于推车的（Philips iE33和Vivid E9）美国机器捕获。质量转换数据集包括来自395例患者的441项回声研究。来自122例患者的POCUS和基于推车的系统的数据均可用。深度传输模型已集成到自动心脏评估任务的管道中，即在AP2视图中对LV进行分割。通过将低质量的POCUS数据传输到基于购物车的美国，LV细分Dice得分和Hausdorff距离度量分别平均提高了30％和34 mm。结论本文提出了一种机器学习解决方案的可行性，该解决方案可以将POCUS数据的图像质量转换为高质量的基于手推车的高端系统。实验表明，通过提出的约束对抗训练，利用质量翻译，可以提高POCUS数据自动分割的准确性。结论本文提出了一种机器学习解决方案的可行性，该解决方案可以将POCUS数据的图像质量转换为高质量的基于手推车的高端系统。实验表明，通过提出的约束对抗训练，利用质量翻译，可以提高POCUS数据自动分割的准确性。结论本文提出了一种机器学习解决方案的可行性，该解决方案可以将POCUS数据的图像质量转换为高质量的基于手推车的高端系统。实验表明，通过提出的约束对抗训练，利用质量翻译，可以提高POCUS数据自动分割的准确性。