https://github.com/kumarshreshtha/image-registration-cnn

A pytorch implementation of CNN based MRI image registration based on Christodoulidis Stergios et al. "Linear and Deformable Image Registration with 3D Convolutional Neural Networks", 2018.
https://github.com/kumarshreshtha/image-registration-cnn

cnn convolutional-neural-networks deep-learning image-registration medical-imaging pytorch

Last synced: 3 months ago
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A pytorch implementation of CNN based MRI image registration based on Christodoulidis Stergios et al. "Linear and Deformable Image Registration with 3D Convolutional Neural Networks", 2018.

• Host: GitHub
• URL: https://github.com/kumarshreshtha/image-registration-cnn
• Owner: kumarshreshtha
• Created: 2019-07-23T19:55:52.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-08-21T18:49:51.000Z (over 2 years ago)
• Last Synced: 2024-08-04T06:01:26.564Z (6 months ago)
• Topics: cnn, convolutional-neural-networks, deep-learning, image-registration, medical-imaging, pytorch
• Language: Python
• Homepage: https://arxiv.org/abs/1809.06226
• Size: 2.03 MB
• Stars: 53
• Watchers: 3
• Forks: 18
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome_medical - image-registration-cnn

README

        
# Linear and Deformable Image Registration with 3D CNN

A minimal PyTorch implementation of CNN based MRI image registration from the paper Christodoulidis Stergios et al. "[Linear and Deformable Image Registration with 3D Convolutional Neural Networks](https://arxiv.org/abs/1809.06226)", 2018.

For a quick introduction to image registration and summary of the paper check out this [presentation](./presentation/Presentation.pdf).

*Note: This is not the official implementation. I had to present this paper for an undergrad internship interview back in 2019 and I ended up writing this quick prototype to go with it. Cleaned it up a bit recently but it's still a barebone running prototype than anything else.*

## Network Architecture

![network architecture from the paper](./presentation/network_architecture.png "Network Architecture")

## Requirements

You can download all requirements for this project using `pip` as follows:

```sh

pip3 install -r requirements.txt

```

## Usage

### Train

As mentioned above, the implementation is pretty barebone. Once you have your data directories set up, you can change the hyperparameters in `train.py` and simply launch the train script.

```sh

python3 train.py

```