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If alone, simple put your name within [] +names: [Thomas Dagonneau] + +# Your project GitHub repository URL +github_repo: https://github.com/brainhack-school2025/Dagonneau_project + +# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty. +website: + +# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`. +# Please only lowercase letters +tags: [project, github, segmentation, brainhack] + +# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects.. + +summary: "This project presents an open-source pipeline for segmenting multiple sclerosis lesions in the spinal cord using multimodal MRI data. Built for the MS-Multi-Spine Challenge, it combines nnUNet, the Spinal Cord Toolbox, Docker, and Boutiques for reproducibility and ease of use. The pipeline includes preprocessing, inference, and post-processing steps, and is packaged with full documentation and containerization to support future research and clinical applications in spinal cord imaging." + +# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name +# below with the extension. +image: "ms_segmentation.png" +--- + + + +## Project definition + +### Background + +Multiple sclerosis (MS) is a chronic disease of the central nervous system, with spinal cord involvement playing a critical role in clinical outcomes. Although MRI is essential for MS diagnosis and disease monitoring [1], spinal cord imaging remains challenging due to anatomical complexity and variability in acquisition protocols. Existing automated lesion segmentation tools have focused primarily on the brain [2–4], and very few methods were designed for spinal cord lesions, particularly across heterogeneous and multimodal MRI data. Additionally, high inter- and intra-rater variability in manual annotations [5] highlights the need for reliable, automated segmentation pipelines. + +In this study, we introduce a novel pipeline for the automatic segmentation of MS lesions in the spinal cord using multiple MRI acquisitions from the same patient. Our approach combines a deep learning model with a robust post-processing framework to produce accurate lesion instance segmentations and estimate prediction uncertainty, addressing the specific challenges of spinal cord imaging. + +### Objectives + +In this project, I focus on the [Multiple Sclerosis Spinal Cord Lesions Detection from MultiSequence MRIs Challenge (MS-Multi-Spine)](https://zenodo.org/records/14051168) and develop a model capable of multimodal MS lesion segmentation. + +- Train a multimodal nnUNet [6] on the challenge’s dataset (private) +- Make the model and pipeline open-source on GitHub (public) +- Containerize the pipeline and provide a Boutiques descriptor to facilitate reuse (public) + +### Tools + +This project uses the following tools: + +- **Terminal**: all code is designed to run in the terminal +- **Python**: all scripts are written in Python +- **Spinal Cord Toolbox (SCT)** [7]: used for preprocessing steps such as coregistration +- **Git and GitHub**: for version control and open access +- **Docker**: to ensure reproducibility +- **Boutiques**: to automate and simplify tool usage + +### Data + +The dataset used in this project is provided by the challenge and is private. It includes: + +- 100 subjects in total +- 50 subjects with T2w + STIR acquisitions +- 25 subjects with T2w + PSIR acquisitions +- 25 subjects with T2w + MP2RAGE acquisitions + +### Deliverables in Week 4 + +- A GitHub repository containing Python scripts for data preprocessing, inference, and postprocessing +- A Dockerfile used to create the Docker image available [here](https://hub.docker.com/repository/docker/tomdag25/ms-seg-challenge2025-multimodal/general) +- A Boutiques descriptor to perform inference + +### Results + +Over the four weeks, I developed a pipeline for the automatic segmentation of MS lesions in a multimodal context. I gained hands-on experience with: + +- **Python scripting**: all processing steps are implemented in Python +- **Git and GitHub**: all project code and resources are versioned and publicly available +- **Spinal Cord Toolbox**: used for preprocessing (e.g., coregistration) +- **Docker**: used to package the pipeline and publish the image to Docker Hub +- **Boutiques**: used to create a descriptor for automated execution of the tool + +### Deliverable 1: GitHub Repository + +This repository contains everything needed to reproduce the training, inference, and submission process. Instructions to train and perform inference are available [here](multimodal-model/training-and-inference-script). + +### Deliverable 2: Docker + +The repository includes the Dockerfile used to build the image available on [Docker Hub](https://hub.docker.com/repository/docker/tomdag25/ms-seg-challenge2025-multimodal/general). Instructions for building the image locally are provided [here](multimodal-model/docker). + +### Deliverable 3: Boutiques Descriptor + +The Boutiques descriptor is available [here](multimodal-model/docker/miccai2025_challenge_descriptor_neuropoly_multimodal.json), with usage instructions [here](multimodal-model/docker). + +### Contribution to Open Science + +This project contributes to open science by: + +- Publishing the complete pipeline code and documentation on GitHub +- Providing a Docker image to ensure reproducibility across environments +- Creating a Boutiques descriptor to facilitate automated reuse by other researchers +- Building on and extending open-source tools such as nnUNet and SCT +- Enabling multimodal spinal cord lesion segmentation—an underexplored area in neuroimaging research + +### Conclusion + +This project presents a complete pipeline for multimodal MS lesion segmentation in the spinal cord, from preprocessing to inference and postprocessing. It leverages state-of-the-art tools and open standards to enable reproducibility and accessibility. The pipeline successfully integrates multimodal imaging data and handles the complexity of spinal cord anatomy, providing reliable segmentation outputs. + +### Next Steps + +- Evaluate the model on additional test data and quantify performance per modality +- Extend support to include missing modalities through imputation or modality-agnostic models +- Release a demo dataset to allow users to test the pipeline without requiring private data +- Submit the pipeline to MICCAI MS Challenge 2025 results and benchmark against other approaches + +### References + +1. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. *Lancet Neurol*. 2018;17: 162–173. +2. Valverde S, Cabezas M, Roura E, González-Villà S, Pareto D, Vilanova JC, et al. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. *Neuroimage*. 2017;155: 159–168. +3. Aslani S, Dayan M, Storelli L, Filippi M, Murino V, Rocca MA, et al. Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. *Neuroimage*. 2019;196: 1–15. +4. Kaur A, Kaur L, Singh A. State-of-the-art segmentation techniques and future directions for multiple sclerosis brain lesions. *Arch Comput Methods Eng*. 2021;28: 951–977. +5. Walsh R, Meurée C, Kerbrat A, Masson A, Hussein BR, Gaubert M, et al. Expert variability and deep learning performance in spinal cord lesion segmentation for multiple sclerosis patients. *2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)*. doi:10.1109/cbms58004.2023.00263 +6. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. *Nat Methods*. 2021;18: 203–211. +7. Leener BD, Lévy S, Dupont SM, Fonov VS, Stikov N, Collins DL, et al. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. *NeuroImage*. 2017;145, Part A: 24–43. diff --git a/content/en/project/Multimodal-ms-lesions-segmentation/ms_segmentation.png b/content/en/project/Multimodal-ms-lesions-segmentation/ms_segmentation.png new file mode 100644 index 00000000..325befd9 Binary files /dev/null and b/content/en/project/Multimodal-ms-lesions-segmentation/ms_segmentation.png differ