# One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking

## TL;DR

This paper introduces a one shot learning method for deformable medical image registration and periodic motion tracking in 3D and 4D datasets, overcoming the need for large training datasets and generalizing to unseen images.

## Contribution

The work presents a novel one shot registration approach using a U-Net and differential spatial transformer for periodic motion tracking in medical imaging.

## Key findings

- Achieves competitive registration accuracy.
- Capable of tracking periodic motion in 3D and 4D datasets.
- Does not require large training datasets.

## Abstract

Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unseen images different from the training datasets. One shot learning comes without the need of large training datasets and has already been proven to be applicable to 3D data. In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets. When applied to 3D dataset the algorithm calculates the inverse of a registration vector field simultaneously. For registration we employed a U-Net combined with a coarse to fine approach and a differential spatial transformer module. The algorithm was thoroughly tested with multiple 4D and 3D datasets publicly available. The results show that the presented approach is able to track periodic motion and to yield a competitive registration accuracy. Possible applications are the use as a stand-alone algorithm for 3D and 4D motion tracking or in the beginning of studies until enough datasets for a separate training phase are available.