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Isfahan Artificial Intelligence Event 2023: Lesion Segmentation and Localization in Magnetic Resonance Images of Patients With Multiple Sclerosis Publisher

Summary: Scientists report deep learning methods improve MS lesion detection in brain scans, aiding better diagnosis. #MultipleSclerosis #MedicalImaging

Davanian F1, 2, 3 ; Adibi I2 ; Tajmirriahi M4 ; Monemian M5 ; Zojaji Z6 ; Montazerolghaem A6 ; Asadinia MA7 ; Mirghaderi SM8 ; Esfahani SAN9 ; Kazemi M7 ; Iravani MR10 ; Shahriari K11 ; Sharifi N10 ; Moharreri S10 Show All Authors
Authors
  1. Davanian F1, 2, 3
  2. Adibi I2
  3. Tajmirriahi M4
  4. Monemian M5
  5. Zojaji Z6
  6. Montazerolghaem A6
  7. Asadinia MA7
  8. Mirghaderi SM8
  9. Esfahani SAN9
  10. Kazemi M7
  11. Iravani MR10
  12. Shahriari K11
  13. Sharifi N10
  14. Moharreri S10
  15. Sedighin F5
  16. Rabbani H4

Source: Journal of Medical Signals and Sensors Published:2025


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. © 2025 Journal of Medical Signals & Sensors.
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