Maulana Azad National Institute of Technology, Bhopal, India.
PeerJ. 2023 Mar 22;11:e14939. doi: 10.7717/peerj.14939. eCollection 2023.
Cardiac magnetic resonance imaging (CMRI) is a non-invasive imaging technique to analyse the structure and function of the heart. It was enhanced considerably over several years to deliver functional information for diagnosing and managing cardiovascular disease. CMRI image delivers non-invasive, clear access to the heart and great vessels. The segmentation of CMRI provides quantification parameters such as myocardial viability, ejection fraction, cardiac chamber volume, and morphological details. In general, experts interpret the CMR images by delineating the images manually. The manual segmentation process is time-consuming, and it has been observed that the final observation varied with the opinion of the different experts. Convolution neural network is a new-age technology that provides impressive results compared to manual ones. In this study convolution neural network model is used for the segmentation task. The neural network parameters have been optimized to perform on the novel data set for accurate predictions. With other parameters, epochs play an essential role in training the network, as the network should not be under-fitted or over-fitted. The relationship between the hyperparameter epoch and accuracy is established in the model. The model delivers the accuracy of 0.88 in terms of the IoU coefficient.
心脏磁共振成像（CMRI）是一种非侵入性成像技术，用于分析心脏的结构和功能。它在过去几年中得到了极大的增强，能够提供用于诊断和治疗心血管疾病的功能信息。CMRI 图像提供了对心脏和大血管的非侵入性、清晰的访问。CMRI 的分割提供了心肌活力、射血分数、心脏腔室容积和形态细节等定量参数。通常，专家通过手动描绘图像来解释 CMR 图像。手动分割过程非常耗时，并且已经观察到最终观察结果因不同专家的意见而有所不同。卷积神经网络是一种新技术，与手动方法相比，它提供了令人印象深刻的结果。在这项研究中，卷积神经网络模型用于分割任务。神经网络参数经过优化，可在新的数据集上进行准确预测。在训练网络时，其他参数中，时期起着至关重要的作用，因为网络既不能欠拟合也不能过拟合。在模型中建立了超参数时期与准确性之间的关系。该模型的 IoU 系数准确率为 0.88。
