Zhang Guangming, Mao Yujie, Li Mingliang, Peng Li, Ling Yunfei, Zhou Xiaobo
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, China.
Front Physiol. 2021 Feb 23;12:613330. doi: 10.3389/fphys.2021.613330. eCollection 2021.
Tetralogy of Fallot (TOF) is a type of congenital cardiac disease with pulmonary artery (PA) stenosis being the most common defect. Repair surgery needs an appropriate patch to enlarge the narrowed artery from the right ventricular (RV) to the PA.
In this work, we proposed a generative adversarial networks (GANs) based method to optimize the patch size, shape, and location. Firstly, we built the 3D PA of patients by segmentation from cardiac computed tomography angiography. After that, normal and stenotic areas of each PA were detected and labeled into two sub-images groups. Then a GAN was trained based on these sub-images. Finally, an optimal prediction model was utilized to repair the PA with patch augmentation in the new patient.
The fivefold cross-validation (CV) was performed for optimal patch prediction based on GANs in the repair of TOF and the CV accuracy was 93.33%, followed by the clinical outcome. This showed that the GAN model has a significant advantage in finding the best balance point of patch optimization.
This approach has the potential to reduce the intraoperative misjudgment rate, thereby providing a detailed surgical plan in patients with TOF.
法洛四联症（TOF）是一种先天性心脏病，肺动脉（PA）狭窄是最常见的缺陷。修复手术需要合适的补片来扩大从右心室（RV）到PA的狭窄动脉。
在这项工作中，我们提出了一种基于生成对抗网络（GANs）的方法来优化补片的大小、形状和位置。首先，我们通过心脏计算机断层扫描血管造影分割构建患者的三维PA。之后，检测每个PA的正常和狭窄区域并将其标记为两个子图像组。然后基于这些子图像训练一个GAN。最后，利用一个最优预测模型对新患者的PA进行补片增强修复。
在TOF修复中基于GANs进行了五折交叉验证（CV）以进行最优补片预测，CV准确率为93.33%，其次是临床结果。这表明GAN模型在找到补片优化的最佳平衡点方面具有显著优势。
这种方法有可能降低术中误判率，从而为TOF患者提供详细的手术方案。
