Introduction

The deepali library is an open source research library for image, point set, and surface registration in PyTorch, which is developed and maintained by the HeartFlow-Imperial College London research lab at the Biomedical Image Analysis Group.

Deepali is a Hindu/Sanskrit Indian given name, which means “joy” as in the gratification one may feel working with code built on a modern tensor library with support for automatic differentiation, and “chain of lamps” alluding to the application of the chain rule by torch.autograd, the concatenation of spatial coordinate transformations, and furthermore the (sequential) composition of PyTorch modules. In addition, the English words “deep” and “ali(-gnment)” partially contained in this name should highlight that this project is not only suitable for traditional non-learning based registration, but in particular facilitates deep learning based approaches to image alignment.

Basics

• Installation

Tutorials

• Pairwise Image Registration

Reference

• Core types and functions
• Data tensors and sets
• Loss functions and metrics
• Predefined layers
• Neural networks
• Spatial transforms
• Miscellaneous utilities

API Reference

• deepali.core
• deepali.data
• deepali.losses
• deepali.modules
• deepali.networks
• deepali.spatial
• deepali.utils

Contributing

• Example Page
• Example Jupyter notebook
• Example MyST notebook