lounesmd / mmstrokenet

MM-StrokeNet method for segmenting sub-acute and chronic stroke lesions.

Deep Learning and Multi-Modal MRI for Segmentation of Sub-Acute and Chronic Stroke Lesions

This repository provides the implementation of the MM-StrokeNet method for segmenting sub-acute and chronic stroke lesions using T1-w and FLAIR MRI modalities.

Main contacts :

Lounès Meddahi [email protected] Francesca Galassi [email protected]

Overview

This repository contains the code for fine-tuning a pre-trained single-modality nnU-Net model to handle two modalities (T1-w and FLAIR MRIs), as well as the following trained models: the baseline single-modality model trained on the ATLAS v2.0 dataset, the fine-tuned single-modality T1-w model, and the fine-tuned dual-modality T1-w + FLAIR model. Fine-tuning was performed on a private dataset. The entire pipeline, the adaptation process, and the models are described in our paper "Deep Learning and Multi-Modal MRI for Segmentation of Sub-Acute and Chronic Stroke Lesions", currelty under-review.

Installation and Requirements

This section provides a step-by-step guide on how to install and run MM-StrokeNet. Before proceeding, ensure your system meets the following requirements:

Software Requirements

• Python 3.9 or higher
• Torch 2.0.0 or higher
• Scipy
• NumPy
• scikit-learn
• scikit-image 0.19.3 or higher

Hardware Used

• NVIDIA GPU (RTX A5000 GPU and 30,6GiB RAM) with CUDA 11.4

Installation Steps

Clone this repository with the following command :

git clone https://github.com/LounesMD/MM_StrokeNet.git

MM-StrokeNet packages

In this repository you will find the following folders :

• Algorithm : Custom scripts used to modify the pretrained nnU-Net model for compatibility with dual-modality (T1-w + FLAIR) inputs.
• Images : Visual assets used in the paper, including images and figures.
• Models : Pretrained and fine-tuned model weights and configurations. This includes:
  • The baseline model trained on the ATLAS v2.0 dataset.
  • The fine-tuned model on T1-weighted MRIs.
  • The fine-tuned model on both T1-weighted and FLAIR MRIs.

Paper :

The original research paper is currently under review. Initial results were presented in the form of an oral presentation at the 13th World Congress for Neurorehabilitation (WCNR) 2024. Here is a link to the paper's abstract.

Citing

If you use this project in your work, please consider citing it as follows:

@misc{MM-STROKEnet,
  authors = {Lounès Meddahi, Stéphanie s Leplaideur, Arthur Masson, Isabelle Bonan, Elise Bannier Francesca Galassi},
  title = {Enhancing stroke lesion detection and segmentation through nnU-net and multi-modal MRI Analysis},
  year = {2024},
  conference = {WCNR 2024 - 13th World Congress for Neurorehabilitation, World federation for Neurorehabilitation},
}