# End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network

## TL;DR

This paper introduces a deep learning approach for deformable image registration that is trained end-to-end in an unsupervised manner, achieving accurate and fast registration on unseen images.

## Contribution

The novel DIRNet architecture combines a ConvNet, spatial transformer, and resampler for end-to-end unsupervised deformable registration, enabling rapid application to new image pairs.

## Key findings

- Registration accuracy comparable to traditional methods
- Significantly reduced execution time
- Effective on diverse image datasets

## Abstract

In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp the moving image to the fixed image. The DIRNet is trained end-to-end by unsupervised optimization of a similarity metric between input image pairs. A trained DIRNet can be applied to perform registration on unseen image pairs in one pass, thus non-iteratively. Evaluation was performed with registration of images of handwritten digits (MNIST) and cardiac cine MR scans (Sunnybrook Cardiac Data). The results demonstrate that registration with DIRNet is as accurate as a conventional deformable image registration method with substantially shorter execution times.