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Journal of Medical Signals and Sensors، جلد ۱۵، شماره ۲، صفحات ۱۰-۴۱۰۳
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چکیده فارسی مقاله
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عنوان انگلیسی Isfahan Artificial Intelligence Event 2023: Lesion Segmentation and Localization in Magnetic Resonance Images of Patients with Multiple Sclerosis
چکیده انگلیسی مقاله 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
کلیدواژه‌های انگلیسی مقاله Lesion detection,magnetic resonance images,multiple sclerosis
نویسندگان مقاله | Fariba Davanian
Department of Neurology, Neuroscience Research Center, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Iman Adibi
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Mahnoosh Tajmirriahi
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Maryam Monemian
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Zahra Zojaji
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Ahmadreza Montazerolghaem
Department of Electrical Engineering, University of Isfahan, Isfahan, Iran


| Mohammad Amin Asadinia
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran


| Seyed Mojtaba Mirghaderi
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran


| Seyed Amin Naji Esfahani
Department of Electrical Engineering, University of Isfahan, Isfahan, Iran


| Mohammad Kazemi
Department of Biomedical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran


| Mohammad Reza Iravani
Department of Biomedical Engineering, Islamic Azad University Science and Research Branch, Tehran, Iran


| Kian Shahriari
Department of Biomedical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran


| Nesa Sharifi
Department of Biomedical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran


| Sadaf Moharreri
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Farnaz Sedighin
Medical Image and Signal Processing Research Center, Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Hossein Rabbani


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/742
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Methodology Articles
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