Highly Accurate and Memory Efficient Unsupervised Learning-Based Discrete CT Registration Using 2.5D Displacement Search.
Mattias P. HeinrichLasse HansenPublished in: MICCAI (3) (2020)
Keyphrases
- highly accurate
- memory efficient
- iterative deepening
- unsupervised learning
- capable of producing
- high quality
- search algorithm
- external memory
- search space
- medical images
- supervised learning
- high accuracy
- ct images
- accurate models
- deformable registration
- clinical applications
- search strategies
- matching process
- three dimensional
- integral image
- evaluation function
- target registration error
- multiple sequence alignment
- registration process
- x ray
- active learning
- training data