CR-Unet-Based Ultrasonic Follicle Monitoring to Reduce Diameter Variability and Generate Area Automatically as a Novel Biomarker for Follicular Maturity.
Identifieur interne : 000345 ( Main/Exploration ); précédent : 000344; suivant : 000346CR-Unet-Based Ultrasonic Follicle Monitoring to Reduce Diameter Variability and Generate Area Automatically as a Novel Biomarker for Follicular Maturity.
Auteurs : Xiaowen Liang [République populaire de Chine] ; Jinghui Fang [République populaire de Chine] ; Haoming Li [République populaire de Chine] ; Xin Yang [République populaire de Chine] ; Dong Ni [République populaire de Chine] ; Fengyi Zeng [République populaire de Chine] ; Zhiyi Chen [République populaire de Chine]Source :
- Ultrasound in medicine & biology [ 1879-291X ] ; 2020.
Abstract
Follicle size is closely related to ovarian function and is an important biomarker in transvaginal ultrasound examinations for assessing follicular maturity during an assisted reproduction cycle. However, manual measurement is time consuming and subject to high inter- and intra- observer variability. Based on the deep learning model CR-Unet described in our previous study, the aim of our present study was to investigate further the feasibility of using this model in clinical practice by validating its performance in reducing the inter- and intra-observer variability of follicle diameter measurement. This study also investigated whether follicular area is a better biomarker than diameter in assessing follicular maturity. Data on 106 ovaries and 230 follicles collected from 80 cases of single follicular cycles and 26 cases of multiple follicular cycles constituted the validation set. Intra-observer variability was 0.973 and 0.982 for the senior sonographer and junior sonographer in single follicular cycles and 0.979 (0.971, 0.985) and 0.920 (0.892, 0.943) in multiple follicular cycles, respectively, while CR-Unet had no intra-group variation. Bland-Altman plot analysis indicated that the 95% limits of agreement between senior sonographer and CR-Unet (-2.1 to 1.1 mm, -2.02 to 0.75 mm) were smaller than those between senior sonographer and junior sonographer (-1.51 to 1.15 mm, -2.1 to 1.56 mm) in single and multiple follicular cycles. The average operating times of diameter measurement taken by the junior sonographer, senior sonographer and CR-Unet were 7.54 ± 1.8, 4.87 ± 0.84 and 1.66 ± 0.76 s, respectively (p < 0.001). Correlation analysis indicated that both manual and automated follicular area correlated better with follicular volume than diameter. The deep learning algorithm and the new biomarker of follicular area hold potential for clinical application of ultrasonic follicular monitoring.
DOI: 10.1016/j.ultrasmedbio.2020.07.020
PubMed: 32839052
Affiliations:
- République populaire de Chine
- Guangdong
- Jiangmen, Sha Tin, Shenzhen
- Université chinoise de Hong Kong
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first="Jinghui" last="Fang">Jinghui Fang</name><affiliation wicri:level="3"><nlm:affiliation>Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Li, Haoming" sort="Li, Haoming" uniqKey="Li H" first="Haoming" last="Li">Haoming Li</name><affiliation wicri:level="3"><nlm:affiliation>Medical Ultrasound Image Computing (MUSIC) Lab, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Medical Ultrasound Image Computing (MUSIC) Lab, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen</wicri:regionArea><placeName><settlement type="city">Shenzhen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Yang, Xin" sort="Yang, Xin" uniqKey="Yang X" first="Xin" last="Yang">Xin Yang</name><affiliation wicri:level="4"><nlm:affiliation>Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Computer Science and Engineering, Chinese University of 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wicri:level="3"><nlm:affiliation>Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Electronic address: zhiyi_chen@gzhmu.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author></analytic><series><title level="j">Ultrasound in medicine & biology</title><idno type="eISSN">1879-291X</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Follicle size is closely related to ovarian function and is an important biomarker in transvaginal ultrasound examinations for assessing follicular maturity during an assisted reproduction cycle. However, manual measurement is time consuming and subject to high inter- and intra- observer variability. Based on the deep learning model CR-Unet described in our previous study, the aim of our present study was to investigate further the feasibility of using this model in clinical practice by validating its performance in reducing the inter- and intra-observer variability of follicle diameter measurement. This study also investigated whether follicular area is a better biomarker than diameter in assessing follicular maturity. Data on 106 ovaries and 230 follicles collected from 80 cases of single follicular cycles and 26 cases of multiple follicular cycles constituted the validation set. Intra-observer variability was 0.973 and 0.982 for the senior sonographer and junior sonographer in single follicular cycles and 0.979 (0.971, 0.985) and 0.920 (0.892, 0.943) in multiple follicular cycles, respectively, while CR-Unet had no intra-group variation. Bland-Altman plot analysis indicated that the 95% limits of agreement between senior sonographer and CR-Unet (-2.1 to 1.1 mm, -2.02 to 0.75 mm) were smaller than those between senior sonographer and junior sonographer (-1.51 to 1.15 mm, -2.1 to 1.56 mm) in single and multiple follicular cycles. The average operating times of diameter measurement taken by the junior sonographer, senior sonographer and CR-Unet were 7.54 ± 1.8, 4.87 ± 0.84 and 1.66 ± 0.76 s, respectively (p < 0.001). Correlation analysis indicated that both manual and automated follicular area correlated better with follicular volume than diameter. The deep learning algorithm and the new biomarker of follicular area hold potential for clinical application of ultrasonic follicular monitoring.</div></front></TEI><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">32839052</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1879-291X</ISSN><JournalIssue CitedMedium="Internet"><Volume>46</Volume><Issue>11</Issue><PubDate><Year>2020</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Ultrasound in medicine & biology</Title><ISOAbbreviation>Ultrasound Med Biol</ISOAbbreviation></Journal><ArticleTitle>CR-Unet-Based Ultrasonic Follicle Monitoring to Reduce Diameter Variability and Generate Area Automatically as a Novel Biomarker for Follicular Maturity.</ArticleTitle><Pagination><MedlinePgn>3125-3134</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0301-5629(20)30335-5</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.ultrasmedbio.2020.07.020</ELocationID><Abstract><AbstractText>Follicle size is closely related to ovarian function and is an important biomarker in transvaginal ultrasound examinations for assessing follicular maturity during an assisted reproduction cycle. However, manual measurement is time consuming and subject to high inter- and intra- observer variability. Based on the deep learning model CR-Unet described in our previous study, the aim of our present study was to investigate further the feasibility of using this model in clinical practice by validating its performance in reducing the inter- and intra-observer variability of follicle diameter measurement. This study also investigated whether follicular area is a better biomarker than diameter in assessing follicular maturity. Data on 106 ovaries and 230 follicles collected from 80 cases of single follicular cycles and 26 cases of multiple follicular cycles constituted the validation set. Intra-observer variability was 0.973 and 0.982 for the senior sonographer and junior sonographer in single follicular cycles and 0.979 (0.971, 0.985) and 0.920 (0.892, 0.943) in multiple follicular cycles, respectively, while CR-Unet had no intra-group variation. Bland-Altman plot analysis indicated that the 95% limits of agreement between senior sonographer and CR-Unet (-2.1 to 1.1 mm, -2.02 to 0.75 mm) were smaller than those between senior sonographer and junior sonographer (-1.51 to 1.15 mm, -2.1 to 1.56 mm) in single and multiple follicular cycles. The average operating times of diameter measurement taken by the junior sonographer, senior sonographer and CR-Unet were 7.54 ± 1.8, 4.87 ± 0.84 and 1.66 ± 0.76 s, respectively (p < 0.001). Correlation analysis indicated that both manual and automated follicular area correlated better with follicular volume than diameter. The deep learning algorithm and the new biomarker of follicular area hold potential for clinical application of ultrasonic follicular monitoring.</AbstractText><CopyrightInformation>Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Liang</LastName><ForeName>Xiaowen</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fang</LastName><ForeName>Jinghui</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Haoming</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Medical Ultrasound Image Computing (MUSIC) Lab, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Xin</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ni</LastName><ForeName>Dong</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Medical Ultrasound Image Computing (MUSIC) Lab, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zeng</LastName><ForeName>Fengyi</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Zhiyi</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Electronic address: zhiyi_chen@gzhmu.edu.cn.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Ultrasound Med Biol</MedlineTA><NlmUniqueID>0410553</NlmUniqueID><ISSNLinking>0301-5629</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Area measurement</Keyword><Keyword MajorTopicYN="N">Deep learning</Keyword><Keyword MajorTopicYN="N">Follicular monitoring</Keyword><Keyword MajorTopicYN="N">In vitro fertilization</Keyword><Keyword MajorTopicYN="N">VOCAL</Keyword><Keyword MajorTopicYN="N">Virtual organ computer-aided analysis</Keyword></KeywordList><CoiStatement>Conflict of interest disclosure No author has any potential conflict of interest.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>03</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>07</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32839052</ArticleId><ArticleId IdType="pii">S0301-5629(20)30335-5</ArticleId><ArticleId IdType="doi">10.1016/j.ultrasmedbio.2020.07.020</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Guangdong</li></region><settlement><li>Jiangmen</li><li>Sha Tin</li><li>Shenzhen</li></settlement><orgName><li>Université chinoise de Hong Kong</li></orgName></list><tree><country name="République populaire de Chine"><region name="Guangdong"><name sortKey="Liang, Xiaowen" sort="Liang, Xiaowen" uniqKey="Liang X" first="Xiaowen" last="Liang">Xiaowen Liang</name></region><name sortKey="Chen, Zhiyi" sort="Chen, Zhiyi" uniqKey="Chen Z" first="Zhiyi" last="Chen">Zhiyi Chen</name><name sortKey="Fang, Jinghui" sort="Fang, Jinghui" uniqKey="Fang J" first="Jinghui" last="Fang">Jinghui Fang</name><name sortKey="Li, Haoming" sort="Li, Haoming" uniqKey="Li H" first="Haoming" last="Li">Haoming Li</name><name sortKey="Ni, Dong" sort="Ni, Dong" uniqKey="Ni D" first="Dong" last="Ni">Dong Ni</name><name sortKey="Yang, Xin" sort="Yang, Xin" uniqKey="Yang X" first="Xin" last="Yang">Xin Yang</name><name sortKey="Zeng, Fengyi" sort="Zeng, Fengyi" uniqKey="Zeng F" first="Fengyi" last="Zeng">Fengyi Zeng</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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