5 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…
Generalised Automatic Anatomy Finder (GAAF): A general framework for 3D location-finding in CT scans
Edward G. A. Henderson, Eliana M. Vasquez Osorio, Marcel van Herk +1
We present GAAF, a Generalised Automatic Anatomy Finder, for the identification of generic anatomical locations in 3D CT scans. GAAF is an end-to-end pipeline, with dedicated modul…
COBRA: Cpu-Only aBdominal oRgan segmentAtion
Edward G. A. Henderson, Dónal M. McSweeney, Andrew F. Green
Abdominal organ segmentation is a difficult and time-consuming task. To reduce the burden on clinical experts, fully-automated methods are highly desirable. Current approaches are…
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…