Deep learning for multi-modal medical image segmentation: a survey and comparative study.
Authors
Affiliations (5)
Affiliations (5)
- Laboratory of Dosing, Analysis and Characterization with High Resolution DAC HR, Ferhat Abbas University, Setif, Algeria.
- Department of Physics, Faculty of Sciences, Ferhat Abbas University, 19000, Sétif, Algeria.
- Department of Electrical Engineering, Faculty of Technology, Ferhat Abbas University, 19000, Sétif, Algeria.
- Laboratory of Dosing, Analysis and Characterization with High Resolution DAC HR, Ferhat Abbas University, Setif, Algeria. [email protected].
- Department of Physics, Faculty of Sciences, Ferhat Abbas University, 19000, Sétif, Algeria. [email protected].
Abstract
For over two decades, medical imaging modalities have played crucial roles in clinical diagnosis. Extracting comprehensive information from a single modality often proves challenging for ensuring clinical accuracy. Consequently, multi-modal medical image fusion methods integrate images from diverse modalities into a single fused image, enhancing information quality and diagnostic reliability. In recent years, deep learning for multi-modal medical image segmentation has emerged as a vibrant research area, yielding promising outcomes. This paper conducts a thorough survey and comparative analysis of advancements in deep learning techniques for multi-modal medical image segmentation from 2019 to 2025. It aims to provide a comprehensive overview of deep learning-based approaches and fusion strategies for integrating information from different imaging modalities. Additionally, the survey highlights how various deep learning models enhance segmentation accuracy and reliability. Common challenges in medical image segmentation are discussed, along side current research trends in the field.