College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China, 518060.
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China, 518060.
Med Image Anal. 2021 Feb;68:101891. doi: 10.1016/j.media.2020.101891. Epub 2020 Nov 11.
Left ventricular (LV) segmentation is essential for the early diagnosis of cardiovascular diseases, which has been reported as the leading cause of death all over the world. However, automated LV segmentation from cardiac magnetic resonance images (CMRI) using the traditional convolutional neural networks (CNNs) is still a challenging task due to the limited labeled CMRI data and low tolerances to irregular scales, shapes and deformations of LV. In this paper, we propose an automated LV segmentation method based on adversarial learning by integrating a multi-stage pose estimation network (MSPN) and a co-discrimination network. Different from existing CNNs, we use a MSPN with multi-scale dilated convolution (MDC) modules to enhance the ranges of receptive field for deep feature extraction. To fully utilize both labeled and unlabeled CMRI data, we propose a novel generative adversarial network (GAN) framework for LV segmentation by combining MSPN with co-discrimination networks. Specifically, the labeled CMRI are first used to initialize our segmentation network (MSPN) and co-discrimination network. Our GAN training includes two different kinds of epochs fed with both labeled and unlabeled CMRI data alternatively, which are different from the traditional CNNs only relied on the limited labeled samples to train the segmentation networks. As both ground truth and unlabeled samples are involved in guiding training, our method not only can converge faster but also obtain a better performance in LV segmentation. Our method is evaluated using MICCAI 2009 and 2017 challenge databases. Experimental results show that our method has obtained promising performance in LV segmentation, which also outperforms the state-of-the-art methods in terms of LV segmentation accuracy from the comparison results.
左心室（LV）分割对于心血管疾病的早期诊断至关重要，据报道，心血管疾病是全球范围内导致死亡的主要原因。然而，由于心脏磁共振图像（CMRI）的有限标记数据和对 LV 的不规则比例、形状和变形的低容忍度，使用传统卷积神经网络（CNNs）从 CMRI 中自动分割 LV 仍然是一项具有挑战性的任务。在本文中，我们提出了一种基于对抗学习的 LV 自动分割方法，该方法结合了多阶段姿态估计网络（MSPN）和共同判别网络。与现有的 CNN 不同，我们使用具有多尺度扩张卷积（MDC）模块的 MSPN 来增强深层特征提取的感受野范围。为了充分利用有标记和无标记的 CMRI 数据，我们提出了一种新颖的生成对抗网络（GAN）框架，用于 LV 分割，该框架结合了 MSPN 和共同判别网络。具体来说，首先使用有标记的 CMRI 来初始化我们的分割网络（MSPN）和共同判别网络。我们的 GAN 训练包括两种不同类型的时期，交替使用有标记和无标记的 CMRI 数据，这与仅依赖于有限的有标记样本来训练分割网络的传统 CNN 不同。由于同时涉及到真实值和无标记样本，我们的方法不仅可以更快地收敛，而且在 LV 分割方面也可以获得更好的性能。我们的方法使用 MICCAI 2009 和 2017 挑战赛数据库进行评估。实验结果表明，我们的方法在 LV 分割方面取得了有希望的性能，并且从比较结果来看，在 LV 分割精度方面也优于最先进的方法。
