CAN3D: Fast 3D medical image segmentation via compact context aggregation.

Dai, Wei; Woo, Boyeong; Liu, Siyu; Marques, Matthew; Engstrom, Craig; Greer, Peter B; Crozier, Stuart; Dowling, Jason A; Chandra, Shekhar S

Dai, Wei; Woo, Boyeong; Liu, Siyu; Marques, Matthew; Engstrom, Craig; Greer, Peter B; Crozier, Stuart; Dowling, Jason A; Chandra, Shekhar S.

Affiliation

Dai W; School of Information Technology and Electrical Engineering, The University of Queensland, Australia. Electronic address: wei.dai@uq.edu.au.
Woo B; School of Information Technology and Electrical Engineering, The University of Queensland, Australia.
Liu S; School of Information Technology and Electrical Engineering, The University of Queensland, Australia.
Marques M; School of Information Technology and Electrical Engineering, The University of Queensland, Australia.
Engstrom C; School of Information Technology and Electrical Engineering, The University of Queensland, Australia.
Greer PB; University of Newcastle, Australia.
Crozier S; School of Information Technology and Electrical Engineering, The University of Queensland, Australia.
Dowling JA; Australian e-Health Research Centre, CSIRO, Australia.
Chandra SS; School of Information Technology and Electrical Engineering, The University of Queensland, Australia.

Med Image Anal ; 82: 102562, 2022 11.

Article de En | MEDLINE | ID: mdl-36049450

Sujet(s)
Mots clés

3D; Deep learning; Magnetic resonance imaging; Prostate and knee; Semantic segmentation

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Traitement d'image par ordinateur / Imagerie tridimensionnelle Type d'étude: Guideline / Prognostic_studies Limites: Humans / Male Langue: En Journal: Med Image Anal Sujet du journal: DIAGNOSTICO POR IMAGEM Année: 2022 Type de document: Article Pays de publication: Pays-Bas

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