Abstract
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.
Highlights
At present, chronic kidney disease has affected approximately 10% of the world’s population, killing millions of people every year, and hundreds of thousands of people undergo dialysis to maintain their lives [1]
The U-shaped fully convolutional neural network (CNN) segmentation model was optimized by incorporating a residual dual-attention module to the model to elevate the accuracy of locating the edge of the cyst, to accurately and quantitatively assess chronic kidney diseases
Studies [18] have shown that the total kidney volume (TKV) is associated with the renal function to a certain extent. e TKV can be estimated by reconstructing the three-dimensional image of the kidney, and the severity of the disease is evaluated. erefore, the threedimensional image of the kidney is reconstructed in the study
Summary
Chronic kidney disease has affected approximately 10% of the world’s population, killing millions of people every year, and hundreds of thousands of people undergo dialysis to maintain their lives [1]. CT scans a part of the human body with X-ray beams to obtain a cross-section or stereo image of the part being examined. It can provide complete three-dimensional information of the part of the body being examined, clearly displaying the organs and structures, as well as the lesions. Xiong et al proposed a tumor segmentation method based on adaptive partitioned firework level sets, which effectively segmented kidney tumors in ultrasound images [10]. The U-shaped fully convolutional neural network (CNN) segmentation model was optimized by incorporating a residual dual-attention module to the model to elevate the accuracy of locating the edge of the cyst, to accurately and quantitatively assess chronic kidney diseases
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