计算机科学
人工智能
分割
Sørensen–骰子系数
卷积神经网络
联营
棱锥(几何)
模式识别(心理学)
编码器
试验装置
深度学习
图像分割
计算机视觉
数学
几何学
操作系统
作者
Shenghan Ren,Yongbing Wang,Rui Hu,Lei Zuo,Liwen Liu,Heng Zhao
出处
期刊:AIP Advances
[American Institute of Physics]
日期:2021-04-01
卷期号:11 (4)
被引量:2
摘要
Quickly and accurately segmenting the left ventricular (LV) myocardium from ultrasound images and measuring the thickness of the interventricular septum and LV wall play an important role in hypertrophic cardiomyopathy. However, the segmentation of the LV myocardium is a challenging task due to image blurring and individual differences. We attempted to perform LV segmentation in ultrasound images using the encoder–decoder architecture of U-Net and other networks and found it to be not accurate enough. Therefore, we propose a novel multi-task cascaded convolutional neural network (called MTC-Net) to segment the LV myocardium from echocardiography. MTC-Net contains two parts: One is pre-trained Resnet-34 followed by two decoder branches for mask and boundary detection, and the other module is pre-trained with many improved novel encoder–decoder architectures for extracting more detailed features. Both parts of the network use the atrous spatial pyramid pooling module to capture high-level text information. A hybrid loss function is engaged for mask and contour prediction. The network is trained and evaluated with echocardiographic images, which are labeled manually by doctors. The comparison study with other networks shows that MTC-Net has better accuracy and performance. MTC-Net achieves state-of-the-art performance on the test set. The mean value of the dice coefficient is 0.9442 and the mean value of intersection over union is 0.8951.
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