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📄 Abstract
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