4 papers
Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications
Edward G. A. Henderson, Marcel van Herk, Andrew F. Green +1
The aim of this study was to develop a model to accurately identify corresponding points between organ segmentations of different patients for radiotherapy applications. A model fo…
The impact of training dataset size and ensemble inference strategies on head and neck auto-segmentation
Edward G. A. Henderson, Marcel van Herk, Eliana M. Vasquez Osorio
Convolutional neural networks (CNNs) are increasingly being used to automate segmentation of organs-at-risk in radiotherapy. Since large sets of highly curated data are scarce, we…
Automatic identification of segmentation errors for radiotherapy using geometric learning
Edward G. A. Henderson, Andrew F. Green, Marcel van Herk +1
Automatic segmentation of organs-at-risk (OARs) in CT scans using convolutional neural networks (CNNs) is being introduced into the radiotherapy workflow. However, these segmentati…
Hounsfield-based Automatic Evaluation of Volumetric Breast Density on Radiotherapy CT-Scans
Deborah E. M. Akuoko, Eliana Vasquez Osorio, Marcel Van Herk +1
Radiotherapy is an integral part of treatment for many patients with breast cancer. However, side effects can occur, Example fibrosis or erythema. If patients at higher risks of ra…