PediDemi -- A Pediatric Demyelinating Lesion Segmentation Dataset

TL;DR

This paper introduces PediDemi, the first publicly available pediatric MRI dataset for demyelinating lesion segmentation, including diverse patient data and demonstrating its importance through model evaluation.

Contribution

It provides a novel pediatric demyelinating lesion dataset with comprehensive metadata, filling a critical gap in available resources for this patient group.

Findings

Model trained on existing MS data performs variably on pediatric cases.

Diverse datasets are crucial for improving lesion segmentation accuracy.

The dataset enables future research in pediatric demyelinating disorders.

Abstract

Demyelinating disorders of the central nervous system may have multiple causes, the most common are infections, autoimmune responses, genetic or vascular etiology. Demyelination lesions are characterized by areas were the myelin sheath of the nerve fibers are broken or destroyed. Among autoimmune disorders, Multiple Sclerosis (MS) is the most well-known Among these disorders, Multiple Sclerosis (MS) is the most well-known and aggressive form. Acute Disseminated Encephalomyelitis (ADEM) is another type of demyelinating disease, typically with a better prognosis. Magnetic Resonance Imaging (MRI) is widely used for diagnosing and monitoring disease progression by detecting lesions. While both adults and children can be affected, there is a significant lack of publicly available datasets for pediatric cases and demyelinating disorders beyond MS. This study introduces, for the first time, a publicly available pediatric dataset for demyelinating lesion segmentation. The dataset comprises MRI scans from 13 pediatric patients diagnosed with demyelinating disorders, including 3 with ADEM. In addition to lesion segmentation masks, the dataset includes extensive patient metadata, such as diagnosis, treatment, personal medical background, and laboratory results. To assess the quality of the dataset and demonstrate its relevance, we evaluate a state-of-the-art lesion segmentation model trained on an existing MS dataset. The results underscore the importance of diverse datasets