nnd 0.2.0

Python library designed to analyze and detect the sclerosis from MRI brain scans using advanced deep learning algorithms

Bachelor’s Thesis – University of Málaga (Sept 2024 – Jun 2025) Author: Emilio Rodrigo Carreira Villalta ([e-mail])

Segmenting sclerosis lesions on 3‑D MRI volumes is a critical yet time‑consuming step in clinical workflows. This repository contains the open‑source code, experiments and analysis accompanying my bachelor’s thesis, which investigates the synergy between YOLOv11 object detection and nnUNet semantic segmentation, enhanced by a novel 3‑D consensus‑ensemble strategy.

Key features

• Dual‑model pipeline – combines fast YOLOv11 localisation with nnUNet high‑resolution segmentation.
• Rotation‑and‑cut consensus – aggregates predictions across multiple axial rotations and random 3‑D crops to mitigate viewpoint bias.
• Statistical benchmarking – leverages the SAES library to run non‑parametric tests and automatically generate LaTeX reports.
• HPC‑ready – training scripts tested on Málaga’s Picasso Supercomputer (SLURM job templates provided).
• Reproducible experiments – configs/ YAML files capture every run; hashes are logged with Weights & Biases.

Getting started

1. Clone & install

git clone https://github.com/rorro6787/neurodegenerative-disease-detector.git
cd neurodegenerative-disease-detector
python3 -m venv venv
source venv/bin/activate

2. Launch experiments

cd models

# Train the different YOLO and nnUNet models
python models_pipeline.py

3. Ensemble inference

An example Jupyter notebook that visualises the different nnUNet model inferences is available at notebooks/predict/visualize_segmentation.ipynb.

4. Statistical analysis

You can observe the statistical analysis results at notebooks/results/experimental_results.ipynb.

Repository structure

├── .github/                  # Github Actions
├── information/              # Relevant Documents
├── neuro_disease_detector/   # Main Library
├── notebooks/                # Exploratory notebooks
├── picasso/                  # Picasso files used for Experiments
├── tests/                    # Test for the package
├── .gitignore          
├── LICENSE.md
├── pyproject.toml
└── README.md

Results

See information/ThesisTFG.pdf for the full ablation study.

Citing

If you use this code, please cite:

@article{carreira2025sclerosis,
  author  = {Emilio Rodrigo Carreira Villalta},
  title   = {Deep Learning–Based MRI Segmentation for Sclerosis Using YOLOv11 and nnUNet},
  journal = {To appear},
  year    = {2025}
}

License

This project is licensed under the MIT License – see LICENSE.md for details.

Acknowledgements

• University of Málaga & SCENIC research group for guidance.
• Málaga Supercomputing Center (Picasso) for GPU resources.
• The open‑source communities behind YOLO, nnUNet and PyTorch.