Department of Cardiology Second Ward, Jingzhou First People's Hospital, No. 8 Hangkang Road, Jingzhou, Hubei Province 434000, China.
Department of Cardiology Third Ward, Jingzhou First People's Hospital, No. 8 Hangkang Road, Jingzhou, Hubei Province 434000, China.
Contrast Media Mol Imaging. 2022 Mar 21;2022:6331206. doi: 10.1155/2022/6331206. eCollection 2022.
This study was aimed to explore the application of cardiac magnetic resonance imaging (MRI) image segmentation model based on U-Net in the diagnosis of a valvular heart disease. The effect of continuous nursing on the survival of discharged patients with cardiac valve replacement was analyzed in this study. In this study, the filling completion operation, cross entropy loss function, and guidance unit were introduced and optimized based on the U-Net network. The heart MRI image segmentation model ML-Net was established. We compared the Dice, Hausdorff distance (HD), and percentage of area difference (PAD) values between ML-Net and other algorithms. The MRI image features of 82 patients with valvular heart disease who underwent cardiac valve replacement were analyzed. According to different nursing methods, they were randomly divided into the control group (routine nursing) and the intervention group (continuous nursing), with 41 cases in each group. The Glasgow Outcome Scale (GOS) score and the Self-rating Anxiety Scale (SAS) were compared between the two groups to assess the degree of anxiety of patients and the survival status at 6 months, 1 year, 2 years, and 3 years after discharge. The results showed that the Dice coefficient, HD, and PAD of the ML-Net algorithm were (0.896 ± 0.071), (5.66 ± 0.45) mm, and (15.34 ± 1.22) %, respectively. The Dice, HD, and PAD values of the ML-Net algorithm were all statistically different from those of the convolutional neural networks (CNN), fully convolutional networks (FCN), SegNet, and U-Net algorithms ( < 0.05). Atrial, ventricular, and aortic abnormalities can be seen in MRI images of patients with valvular heart disease. The cardiac blood flow signal will also be abnormal. The GOS score of the intervention group was significantly higher than that of the control group ( < 0.01). The SAS score was lower than that of the control group ( < 0.05). The survival rates of patients with valvular heart disease at 6 months, 1 year, 2 years, and 3 years after discharge were significantly higher than those in the control group ( < 0.05). The abovementioned results showed that an effective segmentation model for cardiac MRI images was established in this study. Continuous nursing played an important role in the postoperative recovery of discharged patients after cardiac valve replacement. This study provided a reference value for the diagnosis and prognosis of valvular heart disease.
本研究旨在探讨基于 U-Net 的心脏磁共振成像（MRI）图像分割模型在瓣膜性心脏病诊断中的应用。本研究分析了连续护理对心脏瓣膜置换出院患者生存的影响。在本研究中，在 U-Net 网络的基础上，引入并优化了填充完成操作、交叉熵损失函数和指导单元。建立了心脏 MRI 图像分割模型 ML-Net。我们比较了 ML-Net 与其他算法的 Dice、Hausdorff 距离（HD）和面积差异百分比（PAD）值。分析了 82 例接受心脏瓣膜置换术的瓣膜性心脏病患者的 MRI 图像特征。根据不同的护理方法，将他们随机分为对照组（常规护理）和干预组（连续护理），每组 41 例。比较两组患者的格拉斯哥预后量表（GOS）评分和焦虑自评量表（SAS）评分，以评估患者出院后 6 个月、1 年、2 年和 3 年的焦虑程度和生存状况。结果显示，ML-Net 算法的 Dice 系数、HD 和 PAD 分别为（0.896±0.071）、（5.66±0.45）mm 和（15.34±1.22）%。ML-Net 算法的 Dice、HD 和 PAD 值均与卷积神经网络（CNN）、全卷积网络（FCN）、SegNet 和 U-Net 算法有统计学差异（<0.05）。瓣膜性心脏病患者的 MRI 图像可出现心房、心室和主动脉异常，心脏血流信号也会出现异常。干预组的 GOS 评分明显高于对照组（<0.01）。SAS 评分低于对照组（<0.05）。瓣膜性心脏病患者出院后 6 个月、1 年、2 年和 3 年的生存率明显高于对照组（<0.05）。结果表明，本研究建立了一种有效的心脏 MRI 图像分割模型。连续护理对心脏瓣膜置换术后出院患者的术后恢复起着重要作用。本研究为瓣膜性心脏病的诊断和预后提供了参考价值。
