MSLesSeg 4 ICPR

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Overview

This project is based on the recently concluded ICPR challenge organized by the IP Lab at the University of Catania, led by Dr. F. Guarnera and Dr. A. Rondinella. The challenge introduced a carefully curated and refined dataset of brain MRI scans from patients diagnosed with various forms of Multiple Sclerosis. All patients are from the city of Catania; however, the scans were acquired using different MRI machines across multiple hospitals, introducing valuable inter-scanner variability.

The primary objectives of the challenge were twofold: to contribute a novel, versatile dataset to the research community and to benchmark the performance of state-of-the-art (SOTA) models. Participants were encouraged to explore advanced strategies such as sophisticated preprocessing pipelines, variations in image registration spaces, and ensemble methods involving independently trained models.

You may find the published paper here and the challenge’s homepage here.

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Our Solution

• After performing extensive tests with several architectures, such as U-Net, Trans-U-Net and SegFormer3D-based architectures, we found that not only do we obtain comparable results with the SegFormer3D models (compared to larger and more computationally-hungry models such as U-Net) but having less parameters (4.5M) there are practical applications in the medical industry for said model
• Both SegFormer3D and SegFormer3DMoE obtained a DiceScore of 60 on the MSLesSeg Dataset

Setup & Replication

To replicate the experiments you will need to:

1. Acquire the .zip of the MSLesSeg dataset from the authors of the challenge @ IP Lab

2. Extract the dataset into the data directory, specifically into the /data/01-Pre-Processed-Data/ subdirectory

3. Then apply the proper preprocessing scripts

• You can find more details on how to apply the initial preprocessing the data here.
• You can find a terraform script for the provisioning of a cloud instance to replicate the training and experiments performed in this project here.

Environment Setup

The code presented in the repo has the following dependencies

Component	Version
conda	25.1.1
Python	3.11
pip	25.1

To manually setup the environment for experiment replication:

1. Download and install conda

2. Create the virtual environment with conda

conda create --name mslesseg4icpr python=3.11

3. Download all the required modules modules (make sure you are in the root of the project ~/MSLesSeg-4-ICPR)

python -m pip install -r requirements.txt

4. Go into the notebooks directory & launch a jupyter notebook session

jupyter-notebook mslesseg-4-icpr.ipynb

5. Run the entire notebook

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Extended Reality Applications

Since our main proposed model SegFormer3D has about 4.5M parameters and requires around 17 GFLOPs to perform inference, considering a hardware platform such as the META Quest 3 which can perform up to 2.4 TFLOPs, we developed a simple PoC that shows how a surgeon, without compromising the sterility of the operating room can perform inference on a newly received MRI scan, in this case Brain MRI and using the recognized hand gestures with the quest 3, can visualize the inference performed on the MRI scan and better asses the treatment that the patient needs, thus saving precious time and bringing ever closer two of main pillars of our society, technology and healthcare

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System Specks

Hardware

• CPU: Intel i7-8700 (12) @ 4.600GHz
• GPU: NVIDIA GeForce RTX 4070 12GB
• RAM: 32G total

Software

• NVIDIA-DRIVERS: 550.54.14
• CUDA: 12.4

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