Random Forest and Gradient Boosted Trees for Patient Individualized Contrast Agent Dose Reduction in CT Angiography.
René PallenbergMarja FleitmannAndreas Martin StrothJan GerlachAlexander FürschkeJörg BarkhausenArpad BischofHeinz HandelsPublished in: MIE (2023)
Keyphrases
- random forest
- contrast agent
- random forests
- contrast enhanced
- treatment planning
- accurate segmentation
- radiation therapy
- decision trees
- active shape model
- patient data
- computed tomography
- ct images
- heart disease
- blood flow
- mr images
- respiratory motion
- patient specific
- ct data
- automatic segmentation
- intraoperative
- ct scans
- magnetic resonance
- image guided
- x ray
- medical images
- ensemble methods
- fluoroscopic images
- low contrast
- mri data
- multi label
- clinical data
- logistic regression
- image reconstruction
- contrast enhancement
- dce mri
- deformable registration
- feature set
- computer tomography
- medical diagnosis
- pre operative
- nonrigid registration
- base classifiers
- medical imaging
- machine learning
- aortic valve implantation
- prediction accuracy
- naive bayes
- training data
- medical data
- segmentation algorithm
- training set