Authors:
Nada Haj Messaoud
1
;
2
;
Rim Ayari
2
;
Asma Ben Abdallah
2
and
Mohamed Hedi Bedoui
2
Affiliations:
1
Faculty of Sciences of Monastir (FSM), University of Monastir, Monastir, Tunisia
;
2
Medical Technology and Image Processing Laboratory, Faculty of medicine, University of Monastir, Monastir, Tunisia
Keyword(s):
Brain Lobes Segmentation, Deep Learning, Multiple Sclerosis Lesion, U-Net, Features Extraction.
Abstract:
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 extrac
ted from the MRI scans of MS patients.
(More)