Kumar Vikas, Sharma Nitin Mohan, Mahapatra Prasant K, Dogra Neeti, Maurya Lalit, Ahmad Fahad, Dahiya Neelam, Panda Prashant
CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh 160030, India.
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
Diagnostics (Basel). 2025 Mar 9;15(6):663. doi: 10.3390/diagnostics15060663.
: Accurate segmentation of the left ventricle in echocardiograms is crucial for the diagnosis and monitoring of cardiovascular diseases. However, this process is hindered by the limited availability of high-quality annotated datasets and the inherent complexities of echocardiogram images. Traditional methods often struggle to generalize across varying image qualities and conditions, necessitating a more robust solution. : This study aims to enhance left ventricular segmentation in echocardiograms by developing a framework that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation with a MultiResUNet architecture, providing a more accurate and reliable segmentation method. : We propose a GAN-based framework that generates synthetic echocardiogram images and their corresponding segmentation masks, augmenting the available training data. The synthetic data, along with real echocardiograms from the EchoNet-Dynamic dataset, were used to train the MultiResUNet architecture. MultiResUNet incorporates multi-resolution blocks, residual connections, and attention mechanisms to effectively capture fine details at multiple scales. Additional enhancements include atrous spatial pyramid pooling (ASPP) and scaled exponential linear units (SELUs) to further improve segmentation accuracy. : The proposed approach significantly outperforms existing methods, achieving a Dice Similarity Coefficient of 95.68% and an Intersection over Union (IoU) of 91.62%. This represents improvements of 2.58% in Dice and 4.84% in IoU over previous segmentation techniques, demonstrating the effectiveness of GAN-based augmentation in overcoming data scarcity and improving segmentation performance. : The integration of GAN-generated synthetic data and the MultiResUNet architecture provides a robust and accurate solution for left ventricular segmentation in echocardiograms. This approach has the potential to enhance clinical decision-making in cardiovascular medicine by improving the accuracy of automated diagnostic tools, even in the presence of limited and complex training data.
在超声心动图中准确分割左心室对于心血管疾病的诊断和监测至关重要。然而，高质量标注数据集的有限可用性以及超声心动图图像固有的复杂性阻碍了这一过程。传统方法在不同图像质量和条件下往往难以泛化，因此需要一种更强大的解决方案。
本研究旨在通过开发一个框架来增强超声心动图中的左心室分割，该框架将用于合成数据增强的生成对抗网络（GAN）与MultiResUNet架构相结合，提供一种更准确可靠的分割方法。
我们提出了一个基于GAN的框架，该框架生成合成超声心动图图像及其相应的分割掩码，从而增加可用的训练数据。合成数据与来自EchoNet-Dynamic数据集的真实超声心动图一起用于训练MultiResUNet架构。MultiResUNet包含多分辨率块、残差连接和注意力机制，以有效捕捉多个尺度上的精细细节。其他增强措施包括空洞空间金字塔池化（ASPP）和缩放指数线性单元（SELU），以进一步提高分割精度。
所提出的方法显著优于现有方法，达到了95.68%的骰子相似系数和91.62%的交并比（IoU）。与之前的分割技术相比，这分别在骰子系数上提高了2.58%，在IoU上提高了4.84%，证明了基于GAN的增强在克服数据稀缺和提高分割性能方面的有效性。
GAN生成的合成数据与MultiResUNet架构的集成，为超声心动图中的左心室分割提供了一个强大而准确的解决方案。即使在训练数据有限且复杂的情况下，这种方法也有可能通过提高自动诊断工具的准确性来增强心血管医学中的临床决策。
