Abstract：
OBJECTIVE: Myocardial contrast echocardiography (MCE) plays a crucial role in diagnosing ischemia, infarction, masses and other cardiac conditions. In the realm of MCE image analysis, accurate and consistent myocardial segmentation results are essential for enabling automated analysis of various heart diseases. However, current manual diagnostic methods in MCE suffer from poor repeatability and limited clinical applicability. MCE images often exhibit low quality and high noise due to the instability of ultrasound signals, while interference structures can further disrupt segmentation consistency.
METHODS: To overcome these challenges, we proposed a deep-learning network for the segmentation of MCE. This architecture leverages dilated convolutions to capture high-scale information without sacrificing positional accuracy and modifies multi-head self-attention to enhance global context and ensure consistency, effectively overcoming issues related to low image quality and interference. Furthermore, we also adapted the cascade application of transformers with convolutional neural networks for improved segmentation in MCE.
RESULTS: In our experiments, our architecture achieved the best Dice score of 84.35% for standard MCE views compared with that of several state-of-the-art segmentation models. For non-standard views and frames with interfering structures (mass), our models also attained the best Dice scores of 83.33% and 83.97%, respectively.
CONCLUSIONS: These studies proved that our architecture is of excellent shape consistency and robustness, which allows it to deal with segmentation of various types of MCE. Our relatively precise and consistent myocardial segmentation results provide fundamental conditions for the automated analysis of various heart diseases, with the potential to discover underlying pathological features and reduce healthcare costs.
METHODS: To overcome these challenges, we proposed a deep-learning network for the segmentation of MCE. This architecture leverages dilated convolutions to capture high-scale information without sacrificing positional accuracy and modifies multi-head self-attention to enhance global context and ensure consistency, effectively overcoming issues related to low image quality and interference. Furthermore, we also adapted the cascade application of transformers with convolutional neural networks for improved segmentation in MCE.
RESULTS: In our experiments, our architecture achieved the best Dice score of 84.35% for standard MCE views compared with that of several state-of-the-art segmentation models. For non-standard views and frames with interfering structures (mass), our models also attained the best Dice scores of 83.33% and 83.97%, respectively.
CONCLUSIONS: These studies proved that our architecture is of excellent shape consistency and robustness, which allows it to deal with segmentation of various types of MCE. Our relatively precise and consistent myocardial segmentation results provide fundamental conditions for the automated analysis of various heart diseases, with the potential to discover underlying pathological features and reduce healthcare costs.
摘要：
目的：心肌声学造影（MCE）在诊断缺血中起着至关重要的作用。梗塞,肿块和其他心脏病。在MCE图像分析领域，准确和一致的心肌分割结果对于实现各种心脏疾病的自动分析至关重要。然而,当前MCE中的手动诊断方法的可重复性差，临床适用性有限。由于超声信号的不稳定性,MCE图像往往表现出低质量和高噪声,而干扰结构会进一步破坏分割的一致性。
方法：为了克服这些挑战，我们提出了一个用于MCE分割的深度学习网络。这种架构利用扩张卷积来捕获大规模信息，而不牺牲位置准确性，并修改多头自我注意以增强全局上下文并确保一致性，有效地克服了与低图像质量和干扰相关的问题。此外，我们还调整了变压器与卷积神经网络的级联应用，以改善MCE中的分割。
结果：在我们的实验中，与几种最先进的分割模型相比,我们的架构在标准MCE视图中获得了84.35%的最佳Dice评分.对于具有干扰结构（质量）的非标准视图和框架，我们的模型还获得了83.33%和83.97%的最佳骰子得分，分别。
结论：这些研究证明我们的架构具有出色的形状一致性和坚固性，这使得它能够处理各种类型的MCE的分割。我们相对精确和一致的心肌分割结果为自动分析各种心脏病提供了基本条件，有可能发现潜在的病理特征并降低医疗保健成本。
方法：为了克服这些挑战，我们提出了一个用于MCE分割的深度学习网络。这种架构利用扩张卷积来捕获大规模信息，而不牺牲位置准确性，并修改多头自我注意以增强全局上下文并确保一致性，有效地克服了与低图像质量和干扰相关的问题。此外，我们还调整了变压器与卷积神经网络的级联应用，以改善MCE中的分割。
结果：在我们的实验中，与几种最先进的分割模型相比,我们的架构在标准MCE视图中获得了84.35%的最佳Dice评分.对于具有干扰结构（质量）的非标准视图和框架，我们的模型还获得了83.33%和83.97%的最佳骰子得分，分别。
结论：这些研究证明我们的架构具有出色的形状一致性和坚固性，这使得它能够处理各种类型的MCE的分割。我们相对精确和一致的心肌分割结果为自动分析各种心脏病提供了基本条件，有可能发现潜在的病理特征并降低医疗保健成本。
