MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset.
Authors
Affiliations (7)
Affiliations (7)
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy. [email protected].
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy. [email protected].
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
- Department of Drug and Health Sciences, University of Catania, Catania, Italy.
- UOC Radiologia, ARNAS Garibaldi, Catania, Italy.
- Centro Sclerosi Multipla, UOC Neurologia, Azienda Ospedaliera per l'Emergenza Cannizzaro, Catania, Italy.
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy.
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.