Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans.
Carlton ChuJeffrey De FauwNenad TomasevBernardino Romera-ParedesCían HughesJoseph R. LedsamTrevor BackHugh MontgomeryGeraint ReesRosalind RaineKevin SullivanSyed Ali MoinuddinDerek D'SouzaOlaf RonnebergerRuheena MendesJulien CornebisePublished in: F1000Research (2016)
Keyphrases
- automated segmentation
- treatment planning
- medical images
- medical imaging
- ct scans
- computed tomography
- machine learning
- clinical applications
- manual segmentation
- anatomical structures
- ct images
- mri data
- mr images
- magnetic resonance imaging
- magnetic resonance images
- image guided
- imaging modalities
- x ray
- accurate segmentation
- computer aided diagnosis
- segmentation method
- prostate cancer
- ct data
- medical image analysis
- magnetic resonance
- shape analysis
- soft tissue
- model based segmentation
- brain imaging
- three dimensional
- computer vision
- image intensity
- image segmentation
- region of interest
- image analysis
- deformable models
- pattern recognition
- image processing
- image registration
- ground truth
- high quality
- lung cancer