Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10340175.
The segmentation of cardiac chambers is essential for the clinical diagnosis and treatment of cardiovascular diseases. It is demonstrated that in cardiac disease, the left ventricle (LV) is extensively involved. Therefore, segmentation of the LV in echocardiographic images is critical for the precise evaluation of factors that influence cardiac function such as LV volume, ejection fraction, and LV mass. Although these measurements could be obtained by manual segmentation of the LV, it would be time-consuming and inaccurate because of the poor quality and low contrast of these images. Convolutional neural networks, commonly referred to as CNNs, have emerged as a highly favored deep learning technique for medical image segmentation. Despite their popularity, the pooling layers in CNNs ignore the spatial information and do not consider the part-whole hierarchy relationships. Furthermore, they require a large training dataset and a large number of parameters. Therefore, Capsule Networks are proposed to address the CNNs limitations. In this study, for the first time, an optimized capsule-based network for object segmentation called SegCaps is proposed to achieve accurate LV segmentation on echocardiography images applied to the CAMUS dataset. The result was compared against the standard 2D-UNet. The modified SegCaps and 2D-UNet achieved an average Dice similarity coefficient (DSC) of 84.48% and 83.28% on LV segmentation, respectively. The capabilities of the CapsNet led to an improvement of 1.44% in DSC with 92.77% fewer parameters than the U-Net. The results indicate that the proposed method leads to accurate and efficient LV segmentation.Clinical Relevance- From a clinical point of view, our findings lead to more precise evaluations of critical cardiac parameters, including ejection fraction as well as left ventricle volume at end-diastole and end-systole.
心脏腔室的分割对于心血管疾病的临床诊断和治疗至关重要。研究表明，在心脏疾病中，左心室（LV）广泛受累。因此，超声心动图图像中 LV 的分割对于精确评估影响心脏功能的因素（如 LV 容积、射血分数和 LV 质量）至关重要。虽然这些测量值可以通过手动分割 LV 获得，但由于这些图像的质量差、对比度低，因此既耗时又不准确。卷积神经网络，通常称为 CNN，已成为医学图像分割中非常受欢迎的深度学习技术。尽管它们很受欢迎，但 CNN 中的池化层忽略了空间信息，并且不考虑部分与整体的层次关系。此外，它们需要大量的训练数据集和大量的参数。因此，提出了胶囊网络来解决 CNN 的局限性。在这项研究中，首次提出了一种用于对象分割的优化胶囊网络 SegCaps，用于在 CAMUS 数据集上对超声心动图图像进行精确的 LV 分割。结果与标准的 2D-UNet 进行了比较。改进后的 SegCaps 和 2D-UNet 在 LV 分割方面的平均 Dice 相似系数（DSC）分别为 84.48%和 83.28%。CapsNet 的功能使 DSC 提高了 1.44%，而参数比 U-Net 减少了 92.77%。结果表明，该方法能够实现准确高效的 LV 分割。临床意义-从临床角度来看，我们的发现导致了对关键心脏参数的更精确评估，包括射血分数以及舒张末期和收缩末期的左心室容积。
