An adversarial machine-learning-based approach and biomechanically guided validation for improving deformable image registration accuracy between a planning CT and cone-beam CT for adaptive prostate radiotherapy applications.
Anand P. SanthanamMichael LauriaBrad StiehlDaniel ElliottSaty SeshanScott HsiehMinsong CaoDaniel LowPublished in: Medical Imaging: Image Processing (2020)
Keyphrases
- registration accuracy
- cone beam ct
- ct scans
- computed tomography
- machine learning
- image guided
- ct images
- reconstruction method
- projection images
- computer tomography
- image registration
- mutual information
- image alignment
- target registration error
- prostate cancer
- x ray
- medical images
- accurate registration
- registration process
- visual inspection
- medical imaging
- region of interest
- image reconstruction
- prostate segmentation
- registration errors
- displacement field
- three dimensional
- treatment planning
- radiation therapy
- image analysis
- cone beam
- image segmentation
- mr images
- high resolution
- noise level
- prior information
- feature points
- similarity measure
- image processing
- feature selection
- computer vision