北京生物医学工程

基于卷积神经网络和图像显著性的心脏CT图像分割

Cardiac CT image segmentation based on convolutional neural network andimage saliency

作者：赵飞刘杰

单位：北京交通大学生物医学工程系（北京 100044）

分类号：R318.04

出版年·卷·期（页码）：2020·39·1（48-55）

摘要：

目的心脏医学影像中，感兴趣部分的提取与分割是诊断心脏病变部位的关键。由于心脏舒张、收缩以及血液的流动，心脏CT图像易出现弱边界、伪影，传统分割算法易产生过度分割的情况。为此，提出一种基于卷积神经网络和图像显著性的心脏CT图像分割方法。方法采用卷积神经网络对目标区域进行定位，滤除肋骨、肌肉等造影对比不明显部分，截取出感兴趣区域，结合感兴趣区域的对比度，计算并提高感兴趣区域的心脏组织的显著值，通过获得的显著值图像截取心脏图像，并与区域生长算法的分割结果进行对比。最后使用泰州人民医院11例患者的影像数据对算法模型进行训练和测试，其中随机选择9例用于训练，剩余2例用于测试。结果所提算法模型在心底、心中、心尖三个心脏分段的分割正确率分别达到了92.79%、92.79%、94.11%，均优于基于区域生长的分割方法。结论基于卷积神经网络和图像显著性的分割方法能够准确获取心脏的外围轮廓，轮廓边缘更加平滑，完全能够满足CT图像序列的心脏全自动分割任务需求，分割后的图像更加有利于医生对患者心脏健康状况和病变部位的观察。

Objective Extraction and segmentation of interest in cardiac medical imaging is the key to diagnosis of heart disease. As the heart dilation, contraction and blood flowing, cardiac CT images prone to weakboundaries, artifacts, the traditional segmentation algorithm is easy to produce over-segmentation. To this end, this paper presents a CT image segmentation method based on image saliency. Methods This method uses a convolutional neural network to locate the target area, filter out inconspicuous parts such as ribs and muscles, extract the area of interest, combine the contrast of the area of interest, then calculate and improve the saliency value of the heart tissue in the area of interest. The heart image is intercepted from the obtained saliency value image and compared with the segmentation results of the region growing algorithm. Finally, image data of 11 patients from Taizhou People's Hospital are used to train and test the algorithm model, of which 9 cases are randomly selected for training and the remaining 2 cases are used for testing. Results The experimental results show that the segmentation accuracy of the proposed algorithm model in the bottom, middle, apex of the heart is 92.79%, 92.79%, and 94.11%, which are better than the segmentation method based on region growth. Conclusions The segmentation method based on convolutional neural network and image saliency can accurately obtain the peripheral contour of the heart, and the contour edges are smoother, and can fully meet the needs of the automatic heart segmentation task of CT image sequences, the segmented images are more conducive for doctors to observe the patient's heart health and lesions.

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