Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases

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 pati...

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Autores principales:	Xu Fu, Huaiqin Liu, Xiaowang Bi, Xiao Gong
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/94a2baf0e67549a783d176b23dba334a
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spelling	oai:doaj.org-article:94a2baf0e67549a783d176b23dba334a2021-11-08T02:36:00ZDeep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases2040-230910.1155/2021/3774423https://doaj.org/article/94a2baf0e67549a783d176b23dba334a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3774423https://doaj.org/toc/2040-2309This 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.Xu FuHuaiqin LiuXiaowang BiXiao GongHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Xu Fu Huaiqin Liu Xiaowang Bi Xiao Gong Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases
description	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.
format	article
author	Xu Fu Huaiqin Liu Xiaowang Bi Xiao Gong
author_facet	Xu Fu Huaiqin Liu Xiaowang Bi Xiao Gong
author_sort	Xu Fu
title	Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases
title_short	Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases
title_full	Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases
title_fullStr	Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases
title_full_unstemmed	Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases
title_sort	deep-learning-based ct imaging in the quantitative evaluation of chronic kidney diseases
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/94a2baf0e67549a783d176b23dba334a
work_keys_str_mv	AT xufu deeplearningbasedctimaginginthequantitativeevaluationofchronickidneydiseases AT huaiqinliu deeplearningbasedctimaginginthequantitativeevaluationofchronickidneydiseases AT xiaowangbi deeplearningbasedctimaginginthequantitativeevaluationofchronickidneydiseases AT xiaogong deeplearningbasedctimaginginthequantitativeevaluationofchronickidneydiseases
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Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases

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