Left ventricle quantification with sample-level confidence estimation via Bayesian neural network.
Identifieur interne : 000252 ( Main/Exploration ); précédent : 000251; suivant : 000253Left ventricle quantification with sample-level confidence estimation via Bayesian neural network.
Auteurs : Wufeng Xue [République populaire de Chine] ; Tingting Guo [République populaire de Chine] ; Dong Ni [République populaire de Chine]Source :
- Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [ 1879-0771 ] ; 2020.
Abstract
Quantification of cardiac left ventricle has become a hot topic due to its great significance in clinical practice. Many efforts have been devoted to LV quantification and obtained promising performance with the help of various deep neural networks when validated on a group of samples. However, none of them can provide sample-level confidence of the results, i.e., how reliable is the prediction result for one single sample, which would help clinicians make decisions of whether or not to accept the automatic results. In this paper, we achieve this by introducing the uncertainty analysis theory into our LV quantification network. Two types of uncertainty, Model Uncertainty, and Data Uncertainty are analyzed for the quantification performance and contribute to the sample-level confidence. Experiments with data of 145 subjects validate that our method not only improved the quantification performance with an uncertainty-weighted regression loss but also is capable of providing for each sample the confidence level of the estimation results for clinicians' further consideration.
DOI: 10.1016/j.compmedimag.2020.101753
PubMed: 32755759
Affiliations:
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Electronic address: nidong@szu.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen</wicri:regionArea><placeName><settlement type="city">Shenzhen</settlement><region type="province">Guangdong</region></placeName></affiliation></author></analytic><series><title level="j">Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society</title><idno type="eISSN">1879-0771</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">Quantification of cardiac left ventricle has become a hot topic due to its great significance in clinical practice. Many efforts have been devoted to LV quantification and obtained promising performance with the help of various deep neural networks when validated on a group of samples. However, none of them can provide sample-level confidence of the results, i.e., how reliable is the prediction result for one single sample, which would help clinicians make decisions of whether or not to accept the automatic results. In this paper, we achieve this by introducing the uncertainty analysis theory into our LV quantification network. Two types of uncertainty, Model Uncertainty, and Data Uncertainty are analyzed for the quantification performance and contribute to the sample-level confidence. Experiments with data of 145 subjects validate that our method not only improved the quantification performance with an uncertainty-weighted regression loss but also is capable of providing for each sample the confidence level of the estimation results for clinicians' further consideration.</div></front></TEI><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">32755759</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>14</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1879-0771</ISSN><JournalIssue CitedMedium="Internet"><Volume>84</Volume><PubDate><Year>2020</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society</Title><ISOAbbreviation>Comput Med Imaging Graph</ISOAbbreviation></Journal><ArticleTitle>Left ventricle quantification with sample-level confidence estimation via Bayesian neural network.</ArticleTitle><Pagination><MedlinePgn>101753</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0895-6111(20)30056-2</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.compmedimag.2020.101753</ELocationID><Abstract><AbstractText>Quantification of cardiac left ventricle has become a hot topic due to its great significance in clinical practice. Many efforts have been devoted to LV quantification and obtained promising performance with the help of various deep neural networks when validated on a group of samples. However, none of them can provide sample-level confidence of the results, i.e., how reliable is the prediction result for one single sample, which would help clinicians make decisions of whether or not to accept the automatic results. In this paper, we achieve this by introducing the uncertainty analysis theory into our LV quantification network. Two types of uncertainty, Model Uncertainty, and Data Uncertainty are analyzed for the quantification performance and contribute to the sample-level confidence. Experiments with data of 145 subjects validate that our method not only improved the quantification performance with an uncertainty-weighted regression loss but also is capable of providing for each sample the confidence level of the estimation results for clinicians' further consideration.</AbstractText><CopyrightInformation>Copyright © 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Xue</LastName><ForeName>Wufeng</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Guo</LastName><ForeName>Tingting</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ni</LastName><ForeName>Dong</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen, China. Electronic address: nidong@szu.edu.cn.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>26</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Comput Med Imaging Graph</MedlineTA><NlmUniqueID>8806104</NlmUniqueID><ISSNLinking>0895-6111</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Bayesian neural network</Keyword><Keyword MajorTopicYN="N">Left ventricle quantification</Keyword><Keyword MajorTopicYN="N">Monte Carlo Dropout</Keyword><Keyword MajorTopicYN="N">Uncertainty estimate</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>01</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>06</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32755759</ArticleId><ArticleId IdType="pii">S0895-6111(20)30056-2</ArticleId><ArticleId IdType="doi">10.1016/j.compmedimag.2020.101753</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Guangdong</li></region><settlement><li>Shenzhen</li></settlement></list><tree><country name="République populaire de Chine"><region name="Guangdong"><name sortKey="Xue, Wufeng" sort="Xue, Wufeng" uniqKey="Xue W" first="Wufeng" last="Xue">Wufeng Xue</name></region><name sortKey="Guo, Tingting" sort="Guo, Tingting" uniqKey="Guo T" first="Tingting" last="Guo">Tingting Guo</name><name sortKey="Ni, Dong" sort="Ni, Dong" uniqKey="Ni D" first="Dong" last="Ni">Dong Ni</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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