Neural Network Algorithm-Based Three-Dimensional Ultrasound Evaluation in the Diagnosis of Fetal Spina Bifida
In order to realize the automatic recognition and diagnosis in ultrasound images of fetal spina bifida, the U-Net algorithm was improved in this study to obtain a new convolutional neural network algorithm—Oct-U-Net. 3,300 pregnant women were selected as the research objects, who underwent three-dim...
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| Veröffentlicht in: | Scientific programming 2021, Vol.2021, p.1-9 |
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| creator | Chen, Lei Tian, Yingying Deng, Yujie |
| description | In order to realize the automatic recognition and diagnosis in ultrasound images of fetal spina bifida, the U-Net algorithm was improved in this study to obtain a new convolutional neural network algorithm—Oct-U-Net. 3,300 pregnant women were selected as the research objects, who underwent three-dimensional (3D) ultrasound examinations. Then, Oct-U-Net was applied to evaluate the diagnostic effect of fetal spina bifida by recall rate, precise rate, mean standard error, pixel accuracy (PA), mean intersection over union (MIoU), and running time. Besides, the fully convolutional network (FCN) algorithm and the U-Net algorithm were introduced for comparison. Results showed that recall rate, precise rate, PA, and MioU of Oct-U-Net were 0.93, 0.96, 0.949, and 0.917, respectively, which were markedly higher than those of FCN and U-Net P |
| doi_str_mv | 10.1155/2021/3605739 |
| format | Article |
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Then, Oct-U-Net was applied to evaluate the diagnostic effect of fetal spina bifida by recall rate, precise rate, mean standard error, pixel accuracy (PA), mean intersection over union (MIoU), and running time. Besides, the fully convolutional network (FCN) algorithm and the U-Net algorithm were introduced for comparison. Results showed that recall rate, precise rate, PA, and MioU of Oct-U-Net were 0.93, 0.96, 0.949, and 0.917, respectively, which were markedly higher than those of FCN and U-Net P<0.05. The mean standard error of Oct-U-Net was 4.1243, and its average running time was 12.15 seconds. The values of the above two indicators were sharply lower than those of FCN and U-Net P<0.05. In conclusion, Oct-U-Net had a better diagnostic effect on 3D ultrasound images of fetal spina bifida, with higher segmentation accuracy and shorter running time, so it was worthy of clinical application.</description><identifier>ISSN: 1058-9244</identifier><identifier>EISSN: 1875-919X</identifier><identifier>DOI: 10.1155/2021/3605739</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Amniotic fluid ; Artificial neural networks ; Diagnosis ; Evaluation ; Experiments ; Fetuses ; Gestational age ; Image segmentation ; Medical imaging ; Neural networks ; Object recognition ; Recall ; Spina bifida ; Standard error ; Ultrasonic imaging</subject><ispartof>Scientific programming, 2021, Vol.2021, p.1-9</ispartof><rights>Copyright © 2021 Lei Chen et al.</rights><rights>Copyright © 2021 Lei Chen 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><citedby>FETCH-LOGICAL-c337t-a7fc6b047f5f1ba8c1cba0fee5c98ddc422503fe1178ed82fd0f25ec3b61e7cd3</citedby><cites>FETCH-LOGICAL-c337t-a7fc6b047f5f1ba8c1cba0fee5c98ddc422503fe1178ed82fd0f25ec3b61e7cd3</cites><orcidid>0000-0001-7972-6127 ; 0000-0003-3609-4494 ; 0000-0003-0966-7832</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4022,27922,27923,27924</link.rule.ids></links><search><contributor>Abdulhay, Enas</contributor><contributor>Enas Abdulhay</contributor><creatorcontrib>Chen, Lei</creatorcontrib><creatorcontrib>Tian, Yingying</creatorcontrib><creatorcontrib>Deng, Yujie</creatorcontrib><title>Neural Network Algorithm-Based Three-Dimensional Ultrasound Evaluation in the Diagnosis of Fetal Spina Bifida</title><title>Scientific programming</title><description>In order to realize the automatic recognition and diagnosis in ultrasound images of fetal spina bifida, the U-Net algorithm was improved in this study to obtain a new convolutional neural network algorithm—Oct-U-Net. 3,300 pregnant women were selected as the research objects, who underwent three-dimensional (3D) ultrasound examinations. Then, Oct-U-Net was applied to evaluate the diagnostic effect of fetal spina bifida by recall rate, precise rate, mean standard error, pixel accuracy (PA), mean intersection over union (MIoU), and running time. Besides, the fully convolutional network (FCN) algorithm and the U-Net algorithm were introduced for comparison. Results showed that recall rate, precise rate, PA, and MioU of Oct-U-Net were 0.93, 0.96, 0.949, and 0.917, respectively, which were markedly higher than those of FCN and U-Net P<0.05. The mean standard error of Oct-U-Net was 4.1243, and its average running time was 12.15 seconds. The values of the above two indicators were sharply lower than those of FCN and U-Net P<0.05. In conclusion, Oct-U-Net had a better diagnostic effect on 3D ultrasound images of fetal spina bifida, with higher segmentation accuracy and shorter running time, so it was worthy of clinical application.</description><subject>Algorithms</subject><subject>Amniotic fluid</subject><subject>Artificial neural networks</subject><subject>Diagnosis</subject><subject>Evaluation</subject><subject>Experiments</subject><subject>Fetuses</subject><subject>Gestational age</subject><subject>Image segmentation</subject><subject>Medical imaging</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>Recall</subject><subject>Spina bifida</subject><subject>Standard error</subject><subject>Ultrasonic imaging</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>eNp90N9LwzAQB_AiCs7pm39AwEetS5qmPx73U4UxH9zAt5ImlzWzbWaSOvzv7diehYM7jg_H8Q2Ce4KfCWFsFOGIjGiCWUrzi2BAspSFOck_L_sZsyzMozi-Dm6c22FMMoLxIGhW0FleoxX4g7FfaFxvjdW-asIJdyDRurIA4Uw30Dpt2l5uam-5M10r0fyH1x33_R7pFvkK0EzzbWucdsgotADf-4-9bjmaaKUlvw2uFK8d3J37MNgs5uvpa7h8f3mbjpehoDT1IU-VSEocp4opUvJMEFFyrACYyDMpRRxFDFMFhKQZyCxSEquIgaBlQiAVkg6Dh9PdvTXfHThf7Exn--9dEbGE0r5i3KunkxLWOGdBFXurG25_C4KLY6DFMdDiHGjPH0-80q3kB_2__gOpBnbU</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Chen, Lei</creator><creator>Tian, Yingying</creator><creator>Deng, Yujie</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/0000-0001-7972-6127</orcidid><orcidid>https://orcid.org/0000-0003-3609-4494</orcidid><orcidid>https://orcid.org/0000-0003-0966-7832</orcidid></search><sort><creationdate>2021</creationdate><title>Neural Network Algorithm-Based Three-Dimensional Ultrasound Evaluation in the Diagnosis of Fetal Spina Bifida</title><author>Chen, Lei ; Tian, Yingying ; Deng, Yujie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-a7fc6b047f5f1ba8c1cba0fee5c98ddc422503fe1178ed82fd0f25ec3b61e7cd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Amniotic fluid</topic><topic>Artificial neural networks</topic><topic>Diagnosis</topic><topic>Evaluation</topic><topic>Experiments</topic><topic>Fetuses</topic><topic>Gestational age</topic><topic>Image segmentation</topic><topic>Medical imaging</topic><topic>Neural networks</topic><topic>Object recognition</topic><topic>Recall</topic><topic>Spina bifida</topic><topic>Standard error</topic><topic>Ultrasonic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Lei</creatorcontrib><creatorcontrib>Tian, Yingying</creatorcontrib><creatorcontrib>Deng, Yujie</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>Chen, Lei</au><au>Tian, Yingying</au><au>Deng, Yujie</au><au>Abdulhay, Enas</au><au>Enas Abdulhay</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural Network Algorithm-Based Three-Dimensional Ultrasound Evaluation in the Diagnosis of Fetal Spina Bifida</atitle><jtitle>Scientific programming</jtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>9</epage><pages>1-9</pages><issn>1058-9244</issn><eissn>1875-919X</eissn><abstract>In order to realize the automatic recognition and diagnosis in ultrasound images of fetal spina bifida, the U-Net algorithm was improved in this study to obtain a new convolutional neural network algorithm—Oct-U-Net. 3,300 pregnant women were selected as the research objects, who underwent three-dimensional (3D) ultrasound examinations. Then, Oct-U-Net was applied to evaluate the diagnostic effect of fetal spina bifida by recall rate, precise rate, mean standard error, pixel accuracy (PA), mean intersection over union (MIoU), and running time. Besides, the fully convolutional network (FCN) algorithm and the U-Net algorithm were introduced for comparison. Results showed that recall rate, precise rate, PA, and MioU of Oct-U-Net were 0.93, 0.96, 0.949, and 0.917, respectively, which were markedly higher than those of FCN and U-Net P<0.05. The mean standard error of Oct-U-Net was 4.1243, and its average running time was 12.15 seconds. The values of the above two indicators were sharply lower than those of FCN and U-Net P<0.05. 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| subjects | Algorithms Amniotic fluid Artificial neural networks Diagnosis Evaluation Experiments Fetuses Gestational age Image segmentation Medical imaging Neural networks Object recognition Recall Spina bifida Standard error Ultrasonic imaging |
| title | Neural Network Algorithm-Based Three-Dimensional Ultrasound Evaluation in the Diagnosis of Fetal Spina Bifida |
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