Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data

Background and purpose: Convolutional neural networks (CNNs) are increasingly used to automate segmentation for radiotherapy planning, where accurate segmentation of organs-at-risk (OARs) is crucial. Training CNNs often requires large amounts of data. However, large, high quality datasets are scarce...

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Autores principales:	Edward G.A. Henderson, Eliana M. Vasquez Osorio, Marcel van Herk, Andrew F. Green
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Elsevier 2022-04-01
Colección:	Physics and Imaging in Radiation Oncology
Materias:	Limited data 3D convolutional neural network CT scan auto-segmentation
Acceso en línea:	http://www.sciencedirect.com/science/article/pii/S240563162200032X
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