Cargando…
Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients
PURPOSE: Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed the applicability of convolutional neural networks (CNNs) for fully automatic delineation of the GTV from FDG-PET/CT images of patients with head and...
Autores principales: | Moe, Yngve Mardal, Groendahl, Aurora Rosvoll, Tomic, Oliver, Dale, Einar, Malinen, Eirik, Futsaether, Cecilia Marie |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263429/ https://www.ncbi.nlm.nih.gov/pubmed/33559711 http://dx.doi.org/10.1007/s00259-020-05125-x |
Ejemplares similares
-
Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning
por: Groendahl, Aurora Rosvoll, et al.
Publicado: (2023) -
Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics
por: Huynh, Bao Ngoc, et al.
Publicado: (2023) -
A quantitative comparison of gross tumour volumes delineated on [18F]-FDG PET-CT scan and CECT scan in head and neck cancers
por: Venkada, Manickam G, et al.
Publicado: (2012) -
The impact of MRI sequence on tumour staging and gross tumour volume delineation in squamous cell carcinoma of the anal canal
por: Prezzi, Davide, et al.
Publicado: (2017) -
Gross Tumor Delineation in Esophageal Cancer on MRI Compared With (18)F-FDG-PET/CT
por: Vollenbrock, Sophie E., et al.
Publicado: (2019)