Modality preserving U-Net for segmentation of multimodal medical images.

Wu, Bingxuan; Zhang, Fan; Xu, Liang; Shen, Shuwei; Shao, Pengfei; Sun, Mingzhai; Liu, Peng; Yao, Peng; Xu, Ronald X

Wu, Bingxuan; Zhang, Fan; Xu, Liang; Shen, Shuwei; Shao, Pengfei; Sun, Mingzhai; Liu, Peng; Yao, Peng; Xu, Ronald X.

Affiliation

Wu B; Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China.
Zhang F; Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China.
Xu L; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China.
Shen S; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China.
Shao P; Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China.
Sun M; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China.
Liu P; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China.
Yao P; School of Microelectronics, University of Science and Technology of China, Hefei, China.
Xu RX; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China.

Quant Imaging Med Surg ; 13(8): 5242-5257, 2023 Aug 01.

Article de En | MEDLINE | ID: mdl-37581055

Mots clés

Multimodal medical image segmentation; artificial intelligence; semantic segmentation

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Quant Imaging Med Surg Année: 2023 Type de document: Article Pays d'affiliation: Chine

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