NCAadapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation (WACV 2025)

This repository represents the official PyTorch code base for our WACV 2025 published paper NCAadapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation.

Table Of Contents

1. Introduction
2. Installation
3. How to get started?
4. Pre-trained models
5. Citations
6. License

Introduction

This WACV 2025 submission currently includes the following CL baselines

• NCAdapt
• EWC
• RWalk
• SI
• FDR
• DER
• A-Gem

Installation

The simplest way to install all dependencies is by using Anaconda:

1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
3. Navigate to the project root (where setup.py lives).
4. Execute pip install -r requirements.txt to install all required packages.

How to get started?

• Since all U-Net and NCAdapt baselines are implemented in this Framework, all models are trained in the same fashion.
• The easiest way to start is using our train_*.py python files. For every baseline and Continual Learning method, we provide specific train_*.py python files, located in several script folders like this one.
• The eval folder contains a jupyter notebooks that was used to calculate performance metrics and plots used in our submission.

Pre-trained models

• Models: Our pre-trained models from our submission can be provided by contacting the main author upon request.
• Prototypes: Our generated prototypes along with the preprocessed dataset can be requested per mail.

For more information about NCAdapt, please read the following paper:

Ranem, A., Kalkhof, J. & Mukhopadhyay, A. (2024).
NCAdapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation.

Citations

If you are using NCAdapt or our code base for your article, please cite the following paper:

@article{ranem2024ncadapt,
  title={NCAdapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation},
  author={Ranem, Amin and Kalkhof, John and Mukhopadhyay, Anirban},
}

License

Apache License 2.0