Automated Brain Lobe Segmentation and Feature Extraction from Multiple Sclerosis Lesions Using Deep Learning

: This study focuses on automating the segmentation of brain lobes in MRI images of Multiple Sclerosis (MS) lesions to extract crucial features for predicting disability levels. Extracting significant features from MRI images of MS lesions is indeed a complex task due to the variability in lesion characteristics and the detailed nature of MRI images. Furthermore, all these studies required continuous patient monitoring. Therefore, our contribution lies in proposing an approach for the automatic segmentation of brain lobes and the extraction of lesion features (number, size, location, etc.) to predict disability levels in MS patients. To achieve this, we introduced a model inspired by U-Net to perform the segmentation of different brain lobes, aiming to accurately locate the MS lesions. We utilized two private and public databases and achieved an average mean IoU score of 0.70, which can be considered encouraging. Following the segmentation phase, approximately 7200 features were extracted from the MRI scans of MS patients.