VoxelMorph is an open-source deep learning framework designed for medical image registration, a process that aligns multiple medical scans into a common spatial coordinate system. Traditional image registration techniques typically rely on optimization procedures that must be executed separately for each pair of images, which can be computationally expensive and slow. VoxelMorph approaches the problem using neural networks that learn to predict deformation fields that transform one image so that it aligns with another. Once the model has been trained, it can rapidly compute the transformation required to register new image pairs, significantly reducing computational time compared to classical registration algorithms. The framework supports both supervised and unsupervised learning approaches and is commonly used in medical imaging applications such as MRI alignment, anatomical analysis, and longitudinal studies.
Features
- Deep learning framework for deformable medical image registration
- Neural network models that predict deformation fields between images
- Support for both supervised and unsupervised training strategies
- Fast inference once the model has been trained
- Tools for working with 3D volumetric medical imaging data
- Integration with TensorFlow and PyTorch implementations