This review explores deep learning approaches for image segmentation of gliomas, suggesting improvements in multimodal image fusion and diagnostic accuracy.
Key Points
Multimodal image fusion enhances the characterization of gliomas through the integration of diverse imaging techniques.
Deep learning models are vital for accurate segmentation of gliomas, aiding in treatment planning and diagnosis.
The review covers preprocessing methods and evaluation metrics crucial for effective image analysis in gliomas.
Challenges in data management and model interpretability are discussed, highlighting the need for ongoing research in this area.