External validation of deep learning-based contouring of head and neck organs at risk | Zendy

Ellen Brunenberg | Zendy; Isabell K. Steinseifer | Zendy; Sven van den Bosch | Zendy; Johannes H.A.M. Kaanders | Zendy; Charlotte L. Brouwer | Zendy; Mark J. Gooding | Zendy; Wouter van Elmpt | Zendy; René Monshouwer | Zendy

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External validation of deep learning-based contouring of head and neck organs at risk

Author(s) -

Ellen Brunenberg,

Isabell K. Steinseifer,

Sven van den Bosch,

Johannes H.A.M. Kaanders,

Charlotte L. Brouwer,

Mark J. Gooding,

Wouter van Elmpt,

René Monshouwer

Publication year - 2020

Publication title -

physics and imaging in radiation oncology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.777

H-Index - 12

ISSN - 2405-6316

DOI - 10.1016/j.phro.2020.06.006

Subject(s) - contouring , head and neck , percentile , medicine , nuclear medicine , wilcoxon signed rank test , head and neck cancer , radiation therapy , artificial intelligence , medical physics , anatomy , computer science , radiology , surgery , mathematics , statistics , computer graphics (images) , mann–whitney u test

Head and neck (HN) radiotherapy can benefit from automatic delineation of tumor and surrounding organs because of the complex anatomy and the regular need for adaptation. The aim of this study was to assess the performance of a commercially available deep learning contouring (DLC) model on an external validation set.

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