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*Result*: Deep Learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer

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*Title*:
Deep Learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer
*Author/editor-in-chief*:
Holzschuh, Julius (Verfasser); Mix, Michael (Verfasser) ; Ruf, Juri (Verfasser) ; Hölscher, Tobias (Verfasser) ; Kotzerke, Jörg (Verfasser) ; Vrachimis, Alexis (Verfasser) ; Doolan, Paul (Verfasser) ; Ilhan, Harun (Verfasser); Marinescu, Ioana M. (Verfasser); Spohn, Simon Konrad Benedict (Verfasser) ; Fechter, Tobias (Verfasser) ; Kuhn, Dejan (Verfasser); Bronsert, Peter (Verfasser) ; Gratzke, Christian (Verfasser) ; Grosu, Radu (Verfasser) ; Kamran, Sophia C. (Verfasser); Heidari, Pedram (Verfasser); Ng, Thomas S.C (Verfasser); Könik, Arda (Verfasser); Grosu, Anca-Ligia (Verfasser) ; Zamboglou, Constantinos (Verfasser) 
*Publication*:
Freiburg : Universität, 2023
*Physical description scale*:
1 Online-Ressource
*Format*:
*eBook*
*Language*:
*eng*
*Notes*:
Radiotherapy and oncology. - 188 (2023) , 109774, ISSN: 1879-0887
*DOI*:
10.1016/j.radonc.2023.109774

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Deep Learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer / Holzschuh, Julius
HEB512110255

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