Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
Int J Comput Assist Radiol Surg. 2024 Aug;19(8):1597-1605. doi: 10.1007/s11548-024-03182-3. Epub 2024 May 30.
The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CHD present significant challenges to comprehensive heart segmentation.
We proposed a novel two-stage segmentation approach, integrating a Convolutional Neural Network (CNN) with a postprocessing method with conditioned energy function for pulmonary and aorta. The initial stage employs a CNN enhanced by a gated self-attention mechanism for the segmentation of five primary heart structures and two major vessels. Subsequently, the second stage utilizes a conditioned energy function specifically tailored to refine the segmentation of the pulmonary artery and aorta, ensuring vascular continuity.
Our method was evaluated on a public dataset including 110 3D CT volumes, encompassing 16 CHD variants. Compared to prevailing segmentation techniques (U-Net, V-Net, Unetr, dynUnet), our approach demonstrated improvements of 1.02, 1.04, and 1.41% in Dice Coefficient (DSC), Intersection over Union (IOU), and the 95th percentile Hausdorff Distance (HD95), respectively, for heart structure segmentation. For the two great vessels, the enhancements were 1.05, 1.07, and 1.42% in these metrics.
The outcomes on the public dataset affirm the efficacy of our proposed segmentation method. Precise segmentation of the entire heart and great vessels can significantly aid in the diagnosis and treatment of CHD, underscoring the clinical relevance of our findings.
在先天性心脏病（CHD）的 CT 图像中对心脏和大血管进行分割对于心脏畸形的临床评估和 CHD 的诊断至关重要。然而，CHD 固有的多种类型和异常情况给全面的心脏分割带来了重大挑战。
我们提出了一种新颖的两阶段分割方法，将卷积神经网络（CNN）与具有条件能量函数的后处理方法结合起来，用于肺和主动脉的分割。初始阶段使用增强的 CNN 结合门控自注意力机制，对五个主要心脏结构和两个主要血管进行分割。然后，第二阶段利用专门定制的条件能量函数来细化肺动脉和主动脉的分割，确保血管连续性。
我们的方法在一个包含 16 种 CHD 变体的 110 个 3D CT 容积的公共数据集上进行了评估。与现有的分割技术（U-Net、V-Net、Unetr、dynUnet）相比，我们的方法在心脏结构分割方面的 Dice 系数（DSC）、交并比（IOU）和第 95 百分位 Hausdorff 距离（HD95）分别提高了 1.02%、1.04%和 1.41%，对于两个大血管，这些指标分别提高了 1.05%、1.07%和 1.42%。
在公共数据集上的结果证实了我们提出的分割方法的有效性。整个心脏和大血管的精确分割可以显著辅助 CHD 的诊断和治疗，突出了我们研究结果的临床相关性。
