2019年
Automatic segmentation of the uterus on MRI using a convolutional neural network.
Comput. Biol. Medicine
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- 巻
- 114
- 号
- 開始ページ
- 103438
- 終了ページ
- 103438
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1016/j.compbiomed.2019.103438
- 出版者・発行元
- PERGAMON-ELSEVIER SCIENCE LTD
BACKGROUND: This study was performed to evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI by using images of major uterine disorders. METHODS: This study included 122 female patients (14 with uterine endometrial cancer, 15 with uterine cervical cancer, and 55 with uterine leiomyoma). U-net architecture optimized for our research was used for automatic segmentation. Three-fold cross-validation was performed for validation. The results of manual segmentation of the uterus by a radiologist on T2-weighted sagittal images were used as the gold standard. Dice similarity coefficient (DSC) and mean absolute distance (MAD) were used for quantitative evaluation of the automatic segmentation. Visual evaluation using a 4-point scale was performed by two radiologists. DSC, MAD, and the score of the visual evaluation were compared between uteruses with and without uterine disorders. RESULTS: The mean DSC of our model for all patients was 0.82. The mean DSCs for patients with and without uterine disorders were 0.84 and 0.78, respectively (p = 0.19). The mean MADs for patients with and without uterine disorders were 18.5 and 21.4 [pixels], respectively (p = 0.39). The scores of the visual evaluation were not significantly different between uteruses with and without uterine disorders. CONCLUSIONS: Fully automatic uterine segmentation with our modified U-net was clinically feasible. The performance of the segmentation of our model was not influenced by the presence of uterine disorders.
- リンク情報
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- DOI
- https://doi.org/10.1016/j.compbiomed.2019.103438
- DBLP
- https://dblp.uni-trier.de/rec/journals/cbm/KurataNKFYIT19
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/31521902
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000495520100006&DestApp=WOS_CPL
- URL
- https://www.wikidata.org/entity/Q90109927
- URL
- https://dblp.uni-trier.de/db/journals/cbm/cbm114.html#KurataNKFYIT19
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072042020&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85072042020&origin=inward
- ID情報
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- DOI : 10.1016/j.compbiomed.2019.103438
- ISSN : 0010-4825
- eISSN : 1879-0534
- DBLP ID : journals/cbm/KurataNKFYIT19
- PubMed ID : 31521902
- SCOPUS ID : 85072042020
- Web of Science ID : WOS:000495520100006