Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, 400044, China.
Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Comput Biol Med. 2024 Mar;171:108100. doi: 10.1016/j.compbiomed.2024.108100. Epub 2024 Feb 5.
The 2D echocardiography semantic automatic segmentation technique is important in clinical applications for cardiac function assessment and diagnosis of cardiac diseases. However, automatic segmentation of 2D echocardiograms also faces the problems of loss of image boundary information, loss of image localization information, and limitations in data acquisition and annotation. To address these issues, this paper proposes a semi-supervised echocardiography segmentation method. It consists of two models: (1) a boundary attention transformer net (BATNet) and (2) a multi-task level semi-supervised model with consistency constraints on boundary features (semi-BATNet). BATNet is able to capture the location and spatial information of the input feature maps by using the self-attention mechanism. The multi-task level semi-supervised model with boundary feature consistency constraints (semi-BATNet) encourages consistent predictions of boundary features at different scales from the student and teacher networks to calculate the multi-scale consistency loss for unlabeled data. The proposed semi-BATNet was extensively evaluated on the dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS) and self-collected echocardiography dataset from the First Affiliated Hospital of Chongqing Medical University. Experimental results on the CAMUS dataset showed that when only 25% of the images are labeled, the proposed method greatly improved the segmentation performance by utilizing unlabeled images, and it also outperformed five state-of-the-art semi-supervised segmentation methods. Moreover, when only 50% of the images labeled, semi-BATNet achieved the Dice coefficient values of 0.936, the Jaccard similarity of 0.881 on self-collected echocardiography dataset. Semi-BATNet can complete a more accurate segmentation of cardiac structures in 2D echocardiograms, indicating that it has the potential to accurately and efficiently assist cardiologists.
二维超声心动图语义自动分割技术在心脏功能评估和心脏病诊断的临床应用中非常重要。然而，二维超声心动图的自动分割也面临着图像边界信息丢失、图像定位信息丢失以及数据采集和标注的限制等问题。针对这些问题，本文提出了一种半监督的超声心动图分割方法。该方法由两个模型组成：（1）边界注意变换网络（BATNet）和（2）具有边界特征一致性约束的多任务级半监督模型（半 BATNet）。BATNet 能够通过自注意力机制捕捉输入特征图的位置和空间信息。具有边界特征一致性约束的多任务级半监督模型（半 BATNet）鼓励学生网络和教师网络对不同尺度的边界特征进行一致预测，以计算未标记数据的多尺度一致性损失。在心脏多结构超声分割数据集（CAMUS）和重庆医科大学第一附属医院自采集的超声心动图数据集上对所提出的半 BATNet 进行了广泛评估。在 CAMUS 数据集上的实验结果表明，当仅对 25%的图像进行标注时，该方法通过利用未标记图像极大地提高了分割性能，并且也优于五种最先进的半监督分割方法。此外，当仅对 50%的图像进行标注时，半 BATNet 在自采集的超声心动图数据集上实现了 0.936 的 Dice 系数值和 0.881 的 Jaccard 相似性。半 BATNet 可以更准确地完成二维超声心动图中心脏结构的分割，这表明它有潜力帮助心脏病专家进行更准确和高效的诊断。
