A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study

A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contour...

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Detalles Bibliográficos
Autores principales: Kiljunen, Timo, Akram, Saad, Niemelä, Jarkko, Löyttyniemi, Eliisa, Seppälä, Jan, Heikkilä, Janne, Vuolukka, Kristiina, Kääriäinen, Okko-Sakari, Heikkilä, Vesa-Pekka, Lehtiö, Kaisa, Nikkinen, Juha, Gershkevitsh, Eduard, Borkvel, Anni, Adamson, Merve, Zolotuhhin, Daniil, Kolk, Kati, Pang, Eric Pei Ping, Tuan, Jeffrey Kit Loong, Master, Zubin, Chua, Melvin Lee Kiang, Joensuu, Timo, Kononen, Juha, Myllykangas, Mikko, Riener, Maigo, Mokka, Miia, Keyriläinen, Jani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697786/
https://www.ncbi.nlm.nih.gov/pubmed/33212793
http://dx.doi.org/10.3390/diagnostics10110959

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697786/
https://www.ncbi.nlm.nih.gov/pubmed/33212793
http://dx.doi.org/10.3390/diagnostics10110959

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