مروری بر روش‌های بخش‌بندی پلاک‌های ام‌اس در تصاویر ام‌آرآی با استفاده از انواع شبکه‌های U-Net

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشکده مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران

2 استاد، دانشکده مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران

3 دانشیار، دانشکده مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران

4 دانشیار دانشگاه علوم پزشکی شهید صدوقی یزد، یزد، ایران

چکیده

یماری ام‌اس یکی از شایع‌ترین اختلال‌های خودایمنی سیستم عصبی مرکزی است. این بیماری با تخریب غلاف میلین سلول‌های عصبی و ایجاد پلاک‌هایی در بافت مغز و نخاع همراه است. تشخیص دقیق پلاک‌ها در تصاویر ام‌آرآی نقش کلیدی در شناسایی بیماری، پیش‌بینی وضعیت بهبودی بیمار و انتخاب روش درمانی مناسب دارد. در این مقاله با رویکردی مروری و تحلیلی، عملکرد سه معماری پرکاربرد یادگیری عمیق شامل U-Net، ResUNet و AttentionUNet در بخش‌بندی پلاک‌های ام‌اس بررسی و مقایسه شده است. پس از معرفی مجموعه دادگان و معیارهای ارزیابی متداول در این حوزه، نتایج ۳۴ مقاله معتبر با استفاده از معیار Dice تحلیل گردید. معماری‌های U-Net، ResUNet و AttentionUNet به‌طور میانگین به ترتیب امتیاز Dice برابر با 6562/0، 6937/0 و 7435/0 را در بخش‌بندی پلاک‌های ام‌اس در پژوهش‌های مرورشده کسب کرده‌اند. نتایج نشان می‌دهد AttentionUNet عملکرد مناسب‌تری نسبت به سایر روش‌ها دارد. هم‌چنین نقاط قوت و ضعف هر روش تحلیل و مسیرهای آینده پژوهشی در این زمینه پیشنهاد شده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Segmentation Methods for MS Lesions in MRI Images Using U-Net-Based Architectures

نویسندگان [English]

  • Fariba Namiranian 1
  • AliMohammad Latif 2
  • Mahdi Yazdian-Dehkordi 3
  • Abolfazl Nickfarjam 4
1 PhD Student, Computer Engineering Department, Yazd University, Yazd, Iran
2 Professor, Computer Engineering Department, Yazd University, Yazd, Iran
3 Associate Professor, Computer Engineering Department, Yazd University, Yazd, Iran
4 Associate Professor, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
چکیده [English]

MS is one of the most common autoimmune disorders of the central nervous system. It is characterized by the destruction of the myelin sheath of nerve cells and the formation of lesions in brain and spinal cord tissues. Accurate detection of lesions in MRI images plays a crucial role in diagnosing the disease, predicting patient recovery, and selecting appropriate treatment methods. In this article, with a review and analytical approach, the performance of three widely used deep learning architectures including U-Net, ResUNet, and AttentionUNet in the segmentation of MS lesions is examined and compared. After introducing commonly used datasets and evaluation metrics in this field, results from 34 reliable studies were analyzed using the Dice coefficient. On average, U-Net, ResUNet, and AttentionUNet architectures achieved Dice scores of 0.6562, 0.6937, and 0.7435 respectively in MS lesion segmentation in the reviewed studies. The results show that AttentionUNet performs better than other methods. Also, the strengths and weaknesses of each method are analyzed and future research directions in this area are proposed.

کلیدواژه‌ها [English]

  • Image Segmentation
  • Multiple Sclerosis
  • Brain Plaque Detection
  • Attention Module
  • Deep Learning

مراجع

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 3، شماره 1
شهریور 1404
صفحه 69-84

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