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Real-world analysis of manual editing of deep learning contouring in the thorax region

Background and purpose: User-adjustments after deep-learning (DL) contouring in radiotherapy were evaluated to get insight in real-world editing during clinical practice. This study assessed the amount, type and spatial regions of editing of auto-contouring for organs-at-risk (OARs) in routine clini...

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Principais autores: Femke Vaassen, Djamal Boukerroui, Padraig Looney, Richard Canters, Karolien Verhoeven, Stephanie Peeters, Indra Lubken, Jolein Mannens, Mark J. Gooding, Wouter van Elmpt
Formato: Artigo
Idioma:Inglês
Publicado em: Elsevier 2022-04-01
coleção:Physics and Imaging in Radiation Oncology
Assuntos:
Radiotherapy
Automatic segmentation
Auto-contouring
Deep-learning
Contour adjustment
Thorax organs at risk
Acesso em linha:http://www.sciencedirect.com/science/article/pii/S2405631622000379
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