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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Detaylı Bibliyografya
Asıl Yazarlar:	Edward G.A. Henderson, Eliana M. Vasquez Osorio, Marcel van Herk, Andrew F. Green
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Elsevier 2022-04-01
Seri Bilgileri:	Physics and Imaging in Radiation Oncology
Konular:	Limited data 3D convolutional neural network CT scan auto-segmentation
Online Erişim:	http://www.sciencedirect.com/science/article/pii/S240563162200032X
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Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data

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