Objective

In cardiac medical imaging, the extraction and segmentation of the part of interest is the key to the diagnosis of heart disease. Due to irregular diastole and contraction, magnetic resonance imaging (MRI) images have poorly defined boundaries, and traditional segmentation algorithms have poor performance. In this paper, a cardiac MRI segmentation technique using convolutional neural network and image saliency is suggested.

Methods

The convolutional neural network is used for detecting target area, filter out the ribs, muscles and the other parts of the anatomy where the contrast is not clearly defined. It can also be used to extract the region of interest (ROI), and compute the contrast of the ROI in order to improve clarity of the heart tissue within the ROI. The cardiac image diagnosis is performed using the obtained saliency image and compared with the segmentation result of the region growth algorithm. Finally, the images of 85 patients were used to train and test the algorithm model. Here, 46 patients were randomly selected for training, and the remaining 39 were harnessed for further tests.

Results

Segmentation accuracy of our algorithm model in ventricles, septum and the apex of the heart segment reaches 93.14%, 92.58% and 96.21% respectively, which are better than the segmentation method based on the regional growth technique.

Conclusions

The segmentation method using convolutional neural network and image saliency can meet the needs of automatic heart segmentation tasks based on cardiac MRI image sequences. The segmented image is able to assist the doctor to observe the patient's heart health more effectively. As such, our proposed technique has strong potential in clinical applications.

中文翻译：

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基于卷积神经网络的心脏磁共振图像分割

目的

在心脏医学成像中，感兴趣部位的提取和分割是诊断心脏病的关键。由于不规则的舒张和收缩，磁共振成像（MRI）图像的边界定义不佳，而传统的分割算法的性能也较差。本文提出了一种使用卷积神经网络和图像显着度的心脏MRI分割技术。

方法

卷积神经网络用于检测目标区域，滤除肋骨，肌肉和解剖结构中对比度不明确的其他部分。它还可以用于提取感兴趣区域（ROI），并计算ROI的对比度，以提高ROI中心脏组织的清晰度。使用获得的显着图像执行心脏图像诊断，并将其与区域增长算法的分割结果进行比较。最后，将85位患者的图像用于训练和测试算法模型。在这里，随机选择了46位患者进行训练，其余39位患者被利用进行进一步测试。

结果

我们的算法模型在心室，间隔和心尖顶点的分割精度分别达到93.14％，92.58％和96.21％，优于基于区域生长技术的分割方法。

结论

使用卷积神经网络和图像显着度的分割方法可以满足基于心脏MRI图像序列的自动心脏分割任务的需求。分割的图像能够帮助医生更有效地观察患者的心脏健康。因此，我们提出的技术在临床应用中具有强大的潜力。