Analysis of Glomerular Filtration Rate in Ischemic Cerebrovascular Diseases under the Magnetic Resonance Angiography Image Segmentation Algorithm
In order to discuss the segmentation effect of the magnetic resonance angiography (MRA) image segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model and the prognostic value of glomerular filtration rate (GFR) in the ischemic cerebrovascular disease (ICVD), a total of 178 pat...
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| description | In order to discuss the segmentation effect of the magnetic resonance angiography (MRA) image segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model and the prognostic value of glomerular filtration rate (GFR) in the ischemic cerebrovascular disease (ICVD), a total of 178 patients who were admitted to the hospital and received MRA due to ICVD were selected as the research objects of this study. Blood vessel segmentation was performed on the MRA image by fuzzy clustering algorithm and DR-CV model, and all patients were divided into a control group (group A), a single-vessel stenosis group (group B), a two-vessel stenosis group (group C), and a multiple-vessel stenosis group (group D). The GFR was estimated by using the dietary modification equation for kidney disease, and the correlation between GFR and the severity of arterial stenosis in patients with ICVD was analyzed. It was found that the results of the Dice similarity index (DSI) of the MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and the integrated model of boundary and regional information (DR-CV model) were all above 85%. The age and GFR values of the four groups of patients were significantly different (P |
| doi_str_mv | 10.1155/2021/8399153 |
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Blood vessel segmentation was performed on the MRA image by fuzzy clustering algorithm and DR-CV model, and all patients were divided into a control group (group A), a single-vessel stenosis group (group B), a two-vessel stenosis group (group C), and a multiple-vessel stenosis group (group D). The GFR was estimated by using the dietary modification equation for kidney disease, and the correlation between GFR and the severity of arterial stenosis in patients with ICVD was analyzed. It was found that the results of the Dice similarity index (DSI) of the MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and the integrated model of boundary and regional information (DR-CV model) were all above 85%. The age and GFR values of the four groups of patients were significantly different (P < 0.05). The proportions of patients in groups C and D in the group with low DFR were significantly different from those in groups A and B (P < 0.01); the proportions of patients in groups A and B in the high-level GFR group had extremely significant differences compared with group D (P < 0.01). Age, GFR, total cholesterol (TC), and high-density lipoprotein-C (HDL-C) were correlated with the degree of arterial stenosis (P < 0.05). It showed that the segmentation effect of MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model was better, and the GFR level can be used as an independent risk factor for the ICVD.</description><identifier>ISSN: 1058-9244</identifier><identifier>EISSN: 1875-919X</identifier><identifier>DOI: 10.1155/2021/8399153</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Age ; Algorithms ; Angiography ; Artificial intelligence ; Blood vessels ; Cholesterol ; Clustering ; Contrast agents ; Disease ; Fuzzy sets ; Hospitals ; Image segmentation ; Kidney diseases ; Kidneys ; Magnetic resonance ; Medical imaging ; Neural networks ; Patients ; Radiation ; Risk analysis ; Surgery</subject><ispartof>Scientific programming, 2021, Vol.2021, p.1-10</ispartof><rights>Copyright © 2021 Yong Ding et al.</rights><rights>Copyright © 2021 Yong Ding et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c294t-e9a8b77ee002eaed61b5119f7b246a326eadf159ecba2d0f4d156239818c5e613</cites><orcidid>0000-0003-0348-6774 ; 0000-0003-1055-6937 ; 0000-0001-9475-3684 ; 0000-0002-2184-8305 ; 0000-0001-5632-5611 ; 0000-0003-3119-1228 ; 0000-0003-2968-0188 ; 0000-0001-5751-449X ; 0000-0002-5865-2349</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4023,27922,27923,27924</link.rule.ids></links><search><contributor>Ramirez, Gustavo</contributor><contributor>Gustavo Ramirez</contributor><creatorcontrib>Ding, Yong</creatorcontrib><creatorcontrib>Liu, Yuebin</creatorcontrib><creatorcontrib>Peng, Cong</creatorcontrib><creatorcontrib>Wang, Huanmei</creatorcontrib><creatorcontrib>Xu, Yuqin</creatorcontrib><creatorcontrib>Jiao, Shengrong</creatorcontrib><creatorcontrib>Xu, Huan</creatorcontrib><creatorcontrib>Zhao, Yan</creatorcontrib><creatorcontrib>Liu, Mingyu</creatorcontrib><title>Analysis of Glomerular Filtration Rate in Ischemic Cerebrovascular Diseases under the Magnetic Resonance Angiography Image Segmentation Algorithm</title><title>Scientific programming</title><description>In order to discuss the segmentation effect of the magnetic resonance angiography (MRA) image segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model and the prognostic value of glomerular filtration rate (GFR) in the ischemic cerebrovascular disease (ICVD), a total of 178 patients who were admitted to the hospital and received MRA due to ICVD were selected as the research objects of this study. Blood vessel segmentation was performed on the MRA image by fuzzy clustering algorithm and DR-CV model, and all patients were divided into a control group (group A), a single-vessel stenosis group (group B), a two-vessel stenosis group (group C), and a multiple-vessel stenosis group (group D). The GFR was estimated by using the dietary modification equation for kidney disease, and the correlation between GFR and the severity of arterial stenosis in patients with ICVD was analyzed. It was found that the results of the Dice similarity index (DSI) of the MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and the integrated model of boundary and regional information (DR-CV model) were all above 85%. The age and GFR values of the four groups of patients were significantly different (P < 0.05). The proportions of patients in groups C and D in the group with low DFR were significantly different from those in groups A and B (P < 0.01); the proportions of patients in groups A and B in the high-level GFR group had extremely significant differences compared with group D (P < 0.01). Age, GFR, total cholesterol (TC), and high-density lipoprotein-C (HDL-C) were correlated with the degree of arterial stenosis (P < 0.05). It showed that the segmentation effect of MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model was better, and the GFR level can be used as an independent risk factor for the ICVD.</description><subject>Age</subject><subject>Algorithms</subject><subject>Angiography</subject><subject>Artificial intelligence</subject><subject>Blood vessels</subject><subject>Cholesterol</subject><subject>Clustering</subject><subject>Contrast agents</subject><subject>Disease</subject><subject>Fuzzy sets</subject><subject>Hospitals</subject><subject>Image segmentation</subject><subject>Kidney diseases</subject><subject>Kidneys</subject><subject>Magnetic resonance</subject><subject>Medical imaging</subject><subject>Neural networks</subject><subject>Patients</subject><subject>Radiation</subject><subject>Risk analysis</subject><subject>Surgery</subject><issn>1058-9244</issn><issn>1875-919X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><recordid>eNp90MFq3DAQgGFTEugm6a0PIOixcaKRLVs6LtsmWUgpJCn0Zsb22NZiS1tJ27KP0TeON7vnnGYOHzPwJ8ln4DcAUt4KLuBWZVqDzD4kC1ClTDXo32fzzqVKtcjzj8lFCBvOQQHni-T_0uK4DyYw17H70U3kdyN6dmfG6DEaZ9kTRmLGsnVoBppMw1bkqfbuL4bmzX4zgTBQYDvbkmdxIPYDe0txtk8UnEXbEFva3rje43bYs_WEPbFn6iey8fhlOfbOmzhMV8l5h2OgT6d5mfy6-_6yekgff96vV8vHtBE6jylpVHVZEnEuCKktoJYAuitrkReYiYKw7UBqamoULe_yFmQhMq1ANZIKyC6TL8e7W-_-7CjEauN2fq4RKiHLnCuV8WxW10fVeBeCp67aejOh31fAq0P06hC9OkWf-dcjH4xt8Z95X78CfvaEGQ</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Ding, Yong</creator><creator>Liu, Yuebin</creator><creator>Peng, Cong</creator><creator>Wang, Huanmei</creator><creator>Xu, Yuqin</creator><creator>Jiao, Shengrong</creator><creator>Xu, Huan</creator><creator>Zhao, Yan</creator><creator>Liu, Mingyu</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-0348-6774</orcidid><orcidid>https://orcid.org/0000-0003-1055-6937</orcidid><orcidid>https://orcid.org/0000-0001-9475-3684</orcidid><orcidid>https://orcid.org/0000-0002-2184-8305</orcidid><orcidid>https://orcid.org/0000-0001-5632-5611</orcidid><orcidid>https://orcid.org/0000-0003-3119-1228</orcidid><orcidid>https://orcid.org/0000-0003-2968-0188</orcidid><orcidid>https://orcid.org/0000-0001-5751-449X</orcidid><orcidid>https://orcid.org/0000-0002-5865-2349</orcidid></search><sort><creationdate>2021</creationdate><title>Analysis of Glomerular Filtration Rate in Ischemic Cerebrovascular Diseases under the Magnetic Resonance Angiography Image Segmentation Algorithm</title><author>Ding, Yong ; Liu, Yuebin ; Peng, Cong ; Wang, Huanmei ; Xu, Yuqin ; Jiao, Shengrong ; Xu, Huan ; Zhao, Yan ; Liu, Mingyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-e9a8b77ee002eaed61b5119f7b246a326eadf159ecba2d0f4d156239818c5e613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age</topic><topic>Algorithms</topic><topic>Angiography</topic><topic>Artificial intelligence</topic><topic>Blood vessels</topic><topic>Cholesterol</topic><topic>Clustering</topic><topic>Contrast agents</topic><topic>Disease</topic><topic>Fuzzy sets</topic><topic>Hospitals</topic><topic>Image segmentation</topic><topic>Kidney diseases</topic><topic>Kidneys</topic><topic>Magnetic resonance</topic><topic>Medical imaging</topic><topic>Neural networks</topic><topic>Patients</topic><topic>Radiation</topic><topic>Risk analysis</topic><topic>Surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ding, Yong</creatorcontrib><creatorcontrib>Liu, Yuebin</creatorcontrib><creatorcontrib>Peng, Cong</creatorcontrib><creatorcontrib>Wang, Huanmei</creatorcontrib><creatorcontrib>Xu, Yuqin</creatorcontrib><creatorcontrib>Jiao, Shengrong</creatorcontrib><creatorcontrib>Xu, Huan</creatorcontrib><creatorcontrib>Zhao, Yan</creatorcontrib><creatorcontrib>Liu, Mingyu</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Scientific programming</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ding, Yong</au><au>Liu, Yuebin</au><au>Peng, Cong</au><au>Wang, Huanmei</au><au>Xu, Yuqin</au><au>Jiao, Shengrong</au><au>Xu, Huan</au><au>Zhao, Yan</au><au>Liu, Mingyu</au><au>Ramirez, Gustavo</au><au>Gustavo Ramirez</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of Glomerular Filtration Rate in Ischemic Cerebrovascular Diseases under the Magnetic Resonance Angiography Image Segmentation Algorithm</atitle><jtitle>Scientific programming</jtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>1058-9244</issn><eissn>1875-919X</eissn><abstract>In order to discuss the segmentation effect of the magnetic resonance angiography (MRA) image segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model and the prognostic value of glomerular filtration rate (GFR) in the ischemic cerebrovascular disease (ICVD), a total of 178 patients who were admitted to the hospital and received MRA due to ICVD were selected as the research objects of this study. Blood vessel segmentation was performed on the MRA image by fuzzy clustering algorithm and DR-CV model, and all patients were divided into a control group (group A), a single-vessel stenosis group (group B), a two-vessel stenosis group (group C), and a multiple-vessel stenosis group (group D). The GFR was estimated by using the dietary modification equation for kidney disease, and the correlation between GFR and the severity of arterial stenosis in patients with ICVD was analyzed. It was found that the results of the Dice similarity index (DSI) of the MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and the integrated model of boundary and regional information (DR-CV model) were all above 85%. The age and GFR values of the four groups of patients were significantly different (P < 0.05). The proportions of patients in groups C and D in the group with low DFR were significantly different from those in groups A and B (P < 0.01); the proportions of patients in groups A and B in the high-level GFR group had extremely significant differences compared with group D (P < 0.01). Age, GFR, total cholesterol (TC), and high-density lipoprotein-C (HDL-C) were correlated with the degree of arterial stenosis (P < 0.05). It showed that the segmentation effect of MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model was better, and the GFR level can be used as an independent risk factor for the ICVD.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2021/8399153</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-0348-6774</orcidid><orcidid>https://orcid.org/0000-0003-1055-6937</orcidid><orcidid>https://orcid.org/0000-0001-9475-3684</orcidid><orcidid>https://orcid.org/0000-0002-2184-8305</orcidid><orcidid>https://orcid.org/0000-0001-5632-5611</orcidid><orcidid>https://orcid.org/0000-0003-3119-1228</orcidid><orcidid>https://orcid.org/0000-0003-2968-0188</orcidid><orcidid>https://orcid.org/0000-0001-5751-449X</orcidid><orcidid>https://orcid.org/0000-0002-5865-2349</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age Algorithms Angiography Artificial intelligence Blood vessels Cholesterol Clustering Contrast agents Disease Fuzzy sets Hospitals Image segmentation Kidney diseases Kidneys Magnetic resonance Medical imaging Neural networks Patients Radiation Risk analysis Surgery |
| title | Analysis of Glomerular Filtration Rate in Ischemic Cerebrovascular Diseases under the Magnetic Resonance Angiography Image Segmentation Algorithm |
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