Accurate segmentation of breast tumor in ultrasound images through joint training and refined segmentation.

Shen, Xiaoyan; Wu, Xinran; Liu, Ruibo; Li, Hong; Yin, Jiandong; Wang, Liangyu; Ma, He

Shen, Xiaoyan; Wu, Xinran; Liu, Ruibo; Li, Hong; Yin, Jiandong; Wang, Liangyu; Ma, He.

Affiliation

Shen X; The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, People's Republic of China.
Wu X; The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, People's Republic of China.
Liu R; The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, People's Republic of China.
Li H; The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, People's Republic of China.
Yin J; The College of Medicine and Biological Information Engineering, Shengjing Hospital of China Medical University, Shenyang 110004, People's Republic of China.
Wang L; The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, People's Republic of China.
Ma H; The College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, People's Republic of China.

Phys Med Biol ; 67(17)2022 09 02.

Article de En | MEDLINE | ID: mdl-35961304

Sujet(s)
Mots clés

breast ultrasound; deep learning; joint training; segmentation; watershed

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Tumeurs du sein / Échographie mammaire Type d'étude: Diagnostic_studies Limites: Female / Humans Langue: En Journal: Phys Med Biol Année: 2022 Type de document: Article

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