Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases.
Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases.
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DOI：
10.1155/2021/3774423
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发表时间：
2021
影响因子：
--
通讯作者：
Gong X
中科院分区：
文献类型：
--
作者：
Fu X;Liu H;Bi X;Gong X
本研究的重点是将深度学习算法应用于CT图像的分割，从而准确定量地诊断慢性肾脏疾病。首先，利用残差双注意模块（RDA模块）对CT图像中的肾囊肿进行自动分割；选择79例肾囊肿患者作为研究对象，其中27例定义为试验组，52例定义为训练组。将测试组的分割结果纳入Dice相似系数（DSC），精度和召回率进行评估。实验结果表明，RDA-UNET模型的损失函数值快速衰减收敛，本研究模型的分割结果与人工标注的结果基本一致，说明该模型在图像分割方面具有较高的精度，能够准确分割出肾脏的轮廓。其次，RDA-UNET模型对左肾的DSC为96.25%，precision为96.34%，recall为96.88%，对右肾的DSC为94.22%，precision为95.34%，recall为94.61%，均优于其他算法。结果表明，本研究算法模型在各评价指标上均优于其他算法。说明了该模型相对于其他算法模型的优势。综上所述，RDA-UNET模型能有效提高CT图像分割的准确性，在慢性肾脏疾病的CT影像定量评估中具有推广价值。
This study focused on the application of deep learning algorithms in the segmentation of CT images, so as to diagnose chronic kidney diseases accurately and quantitatively. First, the residual dual-attention module (RDA module) was used for automatic segmentation of renal cysts in CT images. 79 patients with renal cysts were selected as research subjects, of whom 27 cases were defined as the test group and 52 cases were defined as the training group. The segmentation results of the test group were evaluated factoring into the Dice similarity coefficient (DSC), precision, and recall. The experimental results showed that the loss function value of the RDA-UNET model rapidly decayed and converged, and the segmentation results of the model in the study were roughly the same as those of manual labeling, indicating that the model had high accuracy in image segmentation, and the contour of the kidney can be segmented accurately. Next, the RDA-UNET model achieved 96.25% DSC, 96.34% precision, and 96.88% recall for the left kidney and 94.22% DSC, 95.34% precision, and 94.61% recall for the right kidney, which were better than other algorithms. The results showed that the algorithm model in this study was superior to other algorithms in each evaluation index. It explained the advantages of this model compared with other algorithm models. In conclusion, the RDA-UNET model can effectively improve the accuracy of CT image segmentation, and it is worth of promotion in the quantitative assessment of chronic kidney diseases through CT imaging.
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影响因子：
--
作者：
Song H;Kang W;Zhang Q;Wang S
通讯作者：
Wang S
影响因子：
4.3
作者：
Hu M;Zhong Y;Xie S;Lv H;Lv Z
通讯作者：
Lv Z
进化肾脏科。
影响因子：
6
作者：
Chevalier RL
通讯作者：
Chevalier RL
影响因子：
4.3
作者：
Wan Z;Dong Y;Yu Z;Lv H;Lv Z
通讯作者：
Lv Z
DOI：
10.7507/1001-5515.201902011
发表时间：
2019-12-25
期刊：
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
影响因子：
--
作者：
Xiong, Xiaoliang;Guo, Yi;Xin, Xiaojie
通讯作者：
Xin, Xiaojie