Guarnera Francesco, Rondinella Alessia, Crispino Elena, Russo Giulia, Di Lorenzo Clara, Maimone Davide, Pappalardo Francesco, Battiato Sebastiano
Department of Mathematics and Computer Science, University of Catania, Catania, Italy.
Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
Sci Data. 2025 May 31;12(1):920. doi: 10.1038/s41597-025-05250-y.
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.
本文介绍了MSLesSeg,这是一个新的、可公开获取的MRI数据集,旨在推动多发性硬化症(MS)病变分割的研究。该数据集包含75名患者的115次扫描,包括T1、T2和FLAIR序列,以及从不同来源收集的补充临床数据。经过专家验证的注释提供了高质量的病变分割标签,建立了一个可靠的人工标注数据集用于基准测试。该数据集的一部分与专家科学家共享,目的是将最新的基于人工智能的自动图像分割解决方案与基于专家偏好的手工分割进行比较。此外,还开发了基于人工智能的MSLesSeg病变分割,并根据最新的最先进方法进行了技术验证。这里呈现的数据集、对研究人员贡献的详细分析以及基线结果标志着推进自动MS病变分割研究的一个重要里程碑。