Popa Maria, Vișa Gabriela Adriana, Șofariu Ciprian Radu
Babeș-Bolyai University, Faculty of Mathematics and Computer Science, Department of Computer Science, Mihail Kogălniceanu 1, Cluj-Napoca, Romania.
The Clinical Pediatric Hospital Sibiu, Pompeiu Onofreiu 2-4, Sibiu, Romania.
Sci Data. 2025 Jul 10;12(1):1184. doi: 10.1038/s41597-025-05346-5.
Multiple Sclerosis (MS) is a chronic autoimmune disease that primarily affects the central nervous system and is predominantly diagnosed in adults, making pediatric cases rare and underrepresented in medical research. This paper introduces the first publicly available MRI dataset specifically dedicated to pediatric multiple sclerosis lesion segmentation. The dataset comprises longitudinal MRI scans from 9 pediatric patients, each with between one and six timepoints, with a total of 28 MRI scans. It includes T1-weighted (MPRAGE), T2-weighted, and FLAIR sequences. Additionally, it provides clinical data and initial symptoms for each patient, offering valuable insights into disease progression. Lesion segmentation was performed by senior experts, ensuring high-quality annotations. To demonstrate the dataset's reliability and utility, we evaluated two deep learning models, achieving competitive segmentation performance. This dataset aims to advance research in pediatric MS, improve lesion segmentation models, and contribute to federated learning approaches.
多发性硬化症(MS)是一种慢性自身免疫性疾病,主要影响中枢神经系统,且主要在成年人中被诊断出来,因此儿科病例在医学研究中很罕见且代表性不足。本文介绍了首个专门用于儿科多发性硬化症病变分割的公开可用MRI数据集。该数据集包含9名儿科患者的纵向MRI扫描,每位患者有1至6个时间点,总共28次MRI扫描。它包括T1加权(MPRAGE)、T2加权和FLAIR序列。此外,它还提供了每位患者的临床数据和初始症状,为疾病进展提供了有价值的见解。病变分割由资深专家进行,确保了高质量的标注。为了证明该数据集的可靠性和实用性,我们评估了两种深度学习模型,取得了具有竞争力的分割性能。这个数据集旨在推动儿科MS的研究,改进病变分割模型,并为联邦学习方法做出贡献。