Sirjani Nasim, Moradi Shakiba, Oghli Mostafa Ghelich, Hosseinsabet Ali, Alizadehasl Azin, Yadollahi Mona, Shiri Isaac, Shabanzadeh Ali
Research and Development Department, Med Fanavarn Plus Co., 10th St. Shahid Babaee Blvd., Payam Special Zone, 3187411213, Karaj, Iran.
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Insights Imaging. 2022 Apr 8;13(1):69. doi: 10.1186/s13244-022-01212-9.
Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate cardiac measurements using a deep neural network. EchoRCNN is a semi-automated neural network for echocardiography sequence segmentation using a combination of mask region-based convolutional neural network image segmentation structure with reference-guided mask propagation video object segmentation network.
The proposed method accurately segments the left and right ventricle regions in four-chamber view echocardiography series with a dice similarity coefficient of 94.03% and 94.97%, respectively. Further post-processing procedures on the segmented left and right ventricle regions resulted in a mean absolute error of 3.13% and 2.03% for ejection fraction and fractional area change parameters, respectively.
This study has achieved excellent performance on the left and right ventricle segmentation, leading to more accurate estimations of vital cardiac parameters such as ejection fraction and fractional area change parameters in the left and right ventricle functionalities, respectively. The results represent that our method can predict an assured, accurate, and reliable cardiac function diagnosis in clinical screenings.
准确评估心脏容积和功能对患有心室功能障碍和心血管疾病的患者具有重要的诊断意义。本研究致力于利用深度神经网络寻找一种可靠的辅助工具，以帮助医生进行更可靠、准确的心脏测量。EchoRCNN是一种半自动神经网络，用于超声心动图序列分割，它结合了基于掩码区域的卷积神经网络图像分割结构和参考引导的掩码传播视频对象分割网络。
所提出的方法在四腔心视图超声心动图序列中准确分割左、右心室区域，其骰子相似系数分别为94.03%和94.97%。对分割后的左、右心室区域进行进一步的后处理程序后，射血分数和面积变化分数参数的平均绝对误差分别为3.13%和2.03%。
本研究在左、右心室分割方面取得了优异的性能，分别对左、右心室功能中的重要心脏参数（如射血分数和面积变化分数参数）进行了更准确的估计。结果表明，我们的方法可以在临床筛查中预测可靠、准确的心脏功能诊断。
