Authors:Fariba Davanian, Iman Adibi, Mahnoosh Tajmirriahi, Maryam Monemian, Zahra Zojaji, Ahmadreza Montazerolghaem, Mohammad Amin Asadinia, Seyed Mojtaba Mirghaderi, Seyed Amin Naji Esfahani, Mohammad Kazemi, Mohammad Reza Iravani, Kian Shahriari, Nesa Sharifi, Sadaf Moharreri, Farnaz Sedighin, Hossein Rabbani
Date published: 2025/2/5
Journal of Medical Signals & Sensors
Vol. 15, Issue. 2

Abstract

Background:
Multiple sclerosis (MS) is one of the most common reasons of neurological disabilities in young adults. The disease occurs when the immune system attacks the central nervous system and destroys the myelin of nervous cells. This results in appearing several lesions in the magnetic resonance (MR) images of patients. Accurate determination of the amount and the place of lesions can help physicians to determine the severity and progress of the disease.
Method:
Due to the importance of this issue, this challenge has been dedicated to the segmentation and localization of lesions in MR images of patients with MS. The goal was to segment and localize the lesions in the flair MR images of patients as close as possible to the ground truth masks.
Results:
Several teams sent us their results for the segmentation and localization of lesions in MR images. Most of the teams preferred to use deep learning methods. The methods varied from a simple U-net structure to more complicated networks.
Conclusion:
The results show that deep learning methods can be useful for segmentation and localization of lesions in MR images. In this study, we briefly described the dataset and the methods of teams attending the competition.