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收稿：2019-11-29，
修回：2020-3-6，
录用：2020-3-13，
纸质出版：2020-09-16
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目的
2
超声图像是临床医学中应用最广泛的医学图像之一，但左心室超声图像一般具有强噪声、弱边缘和组织结构复杂等问题，其图像分割难度较大。临床上需要一种效率高、质量好的超声图像左心室分割算法。本文提出一种基于深层聚合残差密集网络（deep layer aggregation for residual dense network，DLA-RDNet）的超声图像左心室分割算法。
方法
2
对获取的超声图像进行形态学操作，定位目标区域，得到目标图像。构建残差密集网络（residual dense network，RDNet）用于提取图像特征，并将RDNet得到的层次信息通过深层聚合（deep layer aggregation，DLA）的方式紧密融合到一起，得到分割网络DLA-RDNet，用于实现对超声图像左心室的精确分割。通过深监督（deep supervision，DS）方式为网络剪枝，简化网络结构，提升网络运行速度。
结果
2
数据测试集的实验结果表明，所提算法平均准确率为95.68%，平均交并比为97.13%，平均相似性系数为97.15%，平均垂直距离为0.31 mm，分割轮廓合格率为99.32%。与6种分割算法相比，所提算法的分割精度更高。在测试阶段，每幅图像仅需不到1 s的时间即可完成分割，远远超出了专业医生的分割速度。
结论
2
提出了一种深层聚合残差密集神经网络对超声图像左心室进行分割，通过主、客观对比实验表明本文算法的有效性，能够较对比方法更实时准确地对超声图像左心室进行分割，符合临床医学中超声图像左心室分割的需求。
Objective
2
Ultrasound images are widely used in clinical medicine. Compared with other medical imaging technologies
ultrasound(US) images are noninvasive
emit non-ionizing radiation
and are relatively cheap and simple to operate. To assess whether a heart is healthy
the ejection fraction is measured
and the regional wall motion is assessed on the basis of identifying the endocardial border of the left ventricle. Generally
cardiologists analyze and segment ultrasound images in a manual or semiautomatic manner to identify the endocardial border of the left ventricle on ultrasound images. However
these segmentation methods have some disadvantages. On the one hand
they are cumbersome and time-consuming tasks
and these ultrasound images can only be segmented by the professional clinicians. On the other hand
the images must be resegmented for different heart disease patients. These problems can be solved by automatic segmentation systems. Unfortunately
affected by ultrasound imaging device and complex heart structure
left ventricular segmentation suffers from the following challenges: first
false edges lead to incorrect segmentation results because the gray scale of the trabecular and mastoid muscles is similar to the myocardial gray scale. Second
the shapes of the left ventricular heart slice are irregular under the influence of the atrium. Third
the accurate positions of the left ventricles are difficult to obtain from ultrasound images because the gray value of the edges is almost the same with that of the myocardium and the tissues surrounding the left heart (such as fats and lungs). Fourth
ultrasound imaging devices produce substantial noise
which affects the quality of ultrasound images; thus
the resolution of ultrasound images is low and thus not conducive to ventricular structure segmentation. In recent years
algorithms for left ventricular segmentation have considerably improved; however
some problems remain. Compared with traditional segmentation methods
deep learning-based methods are more advanced
but some useful original information is lost when images are processed for downsampling. In addition
these methods hardly recognize the weak edges on ultrasound images
resulting in large errors in edge segmentation. Moreover
their segmentation accuracy is low because of substantial noise on ultrasound images. Considering the abovementioned challenges and problems
this study proposes the use of deep layer aggregation for residual dense networks(DLA-RDNet) to identify the left ventricle endocardial border on two-dimensional ultrasound images.
Method
2
The proposed method includes three parts: image preprocessing
neural network structure
and network optimization. First
the dataset must match the neural network after preprocessing the ultrasound images. This part includes two steps. In the first step
we locate the ventricle on ultrasound images in advance on the basis of prior information to avoid the interference of other tissues and organs. The second step is the expansion of the dataset to prevent overfitting of the network training. Second
a new segmentation network is proposed. On the one hand
we adopt a network connection method called deep layer aggregation(DLA) to make the shallow and deep feature information of images more closely integrated. Therefore
less detailed information is lost in the downsampling and upsampling processes. On the other hand
we redesign the downsampling network(RDNet). Combining the advantages of ResNet and DenseNet
we propose a residual dense network
which allows the downsampling process to retain additional useful information. Third
we optimize the neural network. For the redundant part of the network
we use the deep supervision(DS) method for pruning. Consequently
we simplify the network structure and improve the running speed of the neural network. Furthermore
the network loss function is defined by the combination of binary cross entropy and Dice. We use a sigmoid function to achieve pixel-level classification. Finally
the design of the segmentation network is completed.
Result
2
Experimental results on the test dataset show that the average accuracy of the algorithm is 95.68%
the average cross ratio is 97.13%
Dice is 97.15%
the average vertical distance is 0.31 mm
and the contour yield is 99.32%. Compared with the six segmentation algorithms
the proposed algorithm achieves higher segmentation precision in terms of the recognition of the left ventricle in ultrasound images.
Conclusion
2
A deep layer aggregation for residual dense networks is proposed to segment the left ventricle in ultrasound images. Through subjective and objective evaluations
the effectiveness of the proposed algorithm is verified. The algorithm can accurately segment the left ventricle in ultrasound images in real time
and the segmentation results can meet the strict requirements of left ventricular segmentation in clinical medicine.
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