MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset

This paper presents MSLesSeg, a new, publicly accessible MRI dataset designed to advance research in Multiple Sclerosis (MS) lesion segmentation. The dataset comprises 115 scans of 75 patients including T1, T2 and FLAIR sequences, along with supplementary clinical data collected across different sources. Expert-validated annotations provide high-quality lesion segmentation labels, establishing a reliable human-labeled dataset for benchmarking. Part of the dataset was shared with expert scientists with the aim to compare the last automatic AI-based image segmentation solutions with an expert-biased handmade segmentation. In addition, an AI-based lesion segmentation of MSLesSeg was developed and technically validated against the last state-of-the-art methods. The dataset, the detailed analysis of researcher contributions, and the baseline results presented here mark a significant milestone for advancing automated MS lesion segmentation research.

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Publication Details

Journal
Scientific Data
Published
2025-05-31
DOI
https://doi.org/10.1038/s41597-025-05250-y
Citations
10
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
8.42
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article

MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset

Giulia Russo, Davide Maimone, Alessia Rondinella, Elena Crispino et al.
10 citations
Scientific Data
Brain Tumor Detection and Classification
8.42
article

MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset

Giulia Russo, Davide Maimone, Alessia Rondinella, Elena Crispino, Sebastiano Battiato, Francesco Pappalardo, Francesco Guarnera, Clara Di Lorenzo
article en
10 citations

Abstract

This paper presents MSLesSeg, a new, publicly accessible MRI dataset designed to advance research in Multiple Sclerosis (MS) lesion segmentation. The dataset comprises 115 scans of 75 patients including T1, T2 and FLAIR sequences, along with supplementary clinical data collected across different sources. Expert-validated annotations provide high-quality lesion segmentation labels, establishing a reliable human-labeled dataset for benchmarking. Part of the dataset was shared with expert scientists with the aim to compare the last automatic AI-based image segmentation solutions with an expert-biased handmade segmentation. In addition, an AI-based lesion segmentation of MSLesSeg was developed and technically validated against the last state-of-the-art methods. The dataset, the detailed analysis of researcher contributions, and the baseline results presented here mark a significant milestone for advancing automated MS lesion segmentation research.

Scientific DataVol. 12(1)
University of Catania (IT), Ospedale Garibaldi (IT), Ospedale Cannizzaro (IT)
Openalex Percentile: Top 2%
Brain Tumor Detection and Classification
8.42
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MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset — Giulia Russo, Davide Maimone, et al. · Scientific Data (2025) | TGRS Research Map | TGRS