Impact of Variation Among Experts on the Performance of the Deep Learning Model in MS Lesion Segmentation
Murat Arıcan, Kemal Polat · 2025
Multiple Sclerosis (MS) is a chronic condition caused by the immune system attacking the central nervous system. This process results in demyelination and axonal damage in the brain and spinal cord, leading to the formation of MS lesions. Magnetic Resonance Imaging (MRI) is widely used for diagnosing and monitoring the disease. However, manually labelling lesions is a time-consuming process and subject to variability among experts. In this study, a deep learning-based 3D U-Net model was employed for MS lesion segmentation, and the influence of differing expert annotations on model performance was examined. The ISBI 2015 dataset, annotated by two independent experts, was used for this purpose. The model's training outcomes were evaluated using the Dice Coefficient (F1 Score) and IoU Score (Jaccard Index). The findings underscore the discrepancies between expert opinions. The results of this study indicate that variations in expert annotations impact the model's training performance and reduce its generalizability.