Deep Learning Medical Image Segmentation Methods: A Survey | ||||
| النشرة المعلوماتية في الحاسبات والمعلومات | ||||
| Volume 6, Issue 1 - Serial Number 20240601, January 2024, Page 1-10 PDF (641.12 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/fcihib.2024.189094.1079 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
مى مختار  1; هالة عبد الجليل1; غادة خوريبه1, 2
| ||||
| 1قسم علوم الحاسب، كلية الحاسبات والذكاء الاصطناعي، جامعة حلوان، القاهرة، مصر | ||||
| 2كلية تكنولوجيا المعلومات وعلوم الحاسب، جامعة النيل، الجيزة، مصر | ||||
| Abstract | ||||
| Medical image segmentation is essential for detecting and localizing tumors in medical image analysis. Image segmentation involves the identification of anatomical structures in images. Medical image segmentation starts with manual segmentation using Atlas methods, then auto-segmentation, facilitated by deep learning algorithms. Deep learning-based medical image segmentation retains a significant pledge in reducing treatment planning, radiation-related toxicities, and side effects. This study provides a complete overview of deep-learning medical image segmentation models. We review various deep-learning models and architectures applied to medical image segmentation, including fully convolutional networks, U-Net, and attention-based models. This literature review discusses using different loss functions, data augmentation techniques, and transfer learning in deep learning-based medical image segmentation and several types of medical image modality. Evaluation analysis encloses benchmark datasets for human body organs such as the brain, lungs, chest, and liver. Finally, we summarize the challenges and future directions of deep learning for medical image segmentation. | ||||
Highlights | ||||
| ||||
| Keywords | ||||
| Medical Image Segmentation; Computed To- mography (CT); Magnetic Resonance Imaging (MRI); Deep learning; CNN; U-Net | ||||
| Full Text | ||||
|
| ||||
| References | ||||
|
| ||||
|
Statistics Article View: 258 PDF Download: 222 |
||||
