Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth.

Sartor, Hanna; Minarik, David; Enqvist, Olof; Ulén, Johannes; Wittrup, Anders; Bjurberg, Maria; Trägårdh, Elin

Sartor, Hanna; Minarik, David; Enqvist, Olof; Ulén, Johannes; Wittrup, Anders; Bjurberg, Maria; Trägårdh, Elin.

Afiliação

Sartor H; Diagnostic Radiology, Department of Translational Medicine, Lund University, Skåne University Hospital, Lund, Sweden.
Minarik D; Radiation Physics, Department of Translational Medicine, Lund University, Skåne University Hospital, Malmö, Sweden.
Enqvist O; Eigenvision AB, Malmö, Sweden.
Ulén J; Eigenvision AB, Malmö, Sweden.
Wittrup A; Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital and Department of Clinical Sciences, Lund University, Lund, Sweden.
Bjurberg M; Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden.
Trägårdh E; Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital and Department of Clinical Sciences, Lund University, Lund, Sweden.

Clin Transl Radiat Oncol ; 25: 37-45, 2020 Nov.

Article em En | MEDLINE | ID: mdl-33005756

Palavras-chave

AI, artificial intelligence; Automatic segmentation; CNN, convolutional neural network; CT, computer tomography; CTVN, clinical target volume nodes; Cervical cancer radiotherapy; Clinical Target Volume; Convolutional neural network; MSD, mean surface distance; OAR, organs at risk; Organs-at-risk; PET-CT, positron emission tomography/computer tomography

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Ano de publicação: 2020 Tipo de documento: Article