ABSTRACT

Cardiovascular diseases are leading cause of death worldwide. Timely and accurate detection of disease is required to reduce load on healthcare system and number of deaths. For this, accurate and fast segmentation of Cardiac Magnetic Resonance Images is required. In this study, we propose to develop a transfer learning-based end-to-end trainable method to segment left ventricle, myocardium, and right ventricle of heart. In the presented work, Feature Pyramid Network and U-Net architecture are used where encoder is modified with networks like DenseNet, ResNet, and VGG. Performance evaluation is done using dice score, Jaccard index, and Hausdorff distance according to which U-Net with VGG encoder gives best results. The mean dice score obtained is 0.958, 0.914, and 93.4 for LV, MYO, and RV respectively. Also, Hausdorff distance for the proposed methods is 1.69, 2.28, and 1.90 for LV, MYO, and RV respectively. The p-value for the obtained results is less than 0.05 (=0.0313) which shows the statistical significance of the proposed method. This automatic end-to-end trainable computer-based method requires less time and resources while giving better results than state-of-art methods. It can save the time of medical practitioners in analyzing cardiac diseases.

摘要

心血管疾病是全世界死亡的主要原因。需要及时准确地检测疾病，以减少医疗保健系统的负担和死亡人数。为此，需要准确快速地分割心脏磁共振图像。在这项研究中，我们建议开发一种基于迁移学习的端到端可训练方法来分割心脏的左心室、心肌和右心室。在目前的工作中，使用了特征金字塔网络和 U-Net 架构，其中使用 DenseNet、ResNet 和 VGG 等网络修改了编码器。性能评估是使用骰子分数、Jaccard 指数和 Hausdorff 距离完成的，据此，带有 VGG 编码器的 U-Net 给出了最佳结果。LV、MYO 和 RV 获得的平均骰子分数分别为 0.958、0.914 和 93.4。还，对于 LV、MYO 和 RV，所提出方法的 Hausdorff 距离分别为 1.69、2.28 和 1.90。这所得结果的p值小于 0.05 (=0.0313)，这显示了所提出方法的统计显着性。这种自动端到端可训练的基于计算机的方法需要更少的时间和资源，同时比最先进的方法提供更好的结果。它可以节省医生分析心脏病的时间。