Deep learning for brain tumor segmentation in multimodal MRI images: A review of methods and advances

Background and Objectives:

Image segmentation is crucial in applications like image understanding, feature extraction, and analysis. The rapid development of deep learning techniques in recent years has significantly enhanced the field of medical image processing, with the process of segmenting tumor from MRI images of the brain emerging as a particularly active area of interest within the medical science community. Existing reviews predominantly focus on traditional CNNs and Transformer models but lack systematic analysis and experimental validation on the application of the emerging Mamba architecture in multimodal brain tumor segmentation, the handling of missing modalities, the potential of multimodal fusion strategies, and the heterogeneity of datasets.

Methods:

This paper provides a comprehensive literature review of recent deep learning-based methods for multimodal brain tumor segmentation using multimodal MRI images, including performance and quantitative analysis of state-of-the-art approaches. It focuses on the handling of multimodal fusion, adaptation techniques, and missing modality, while also delving into the performance, advantages, and disadvantages of deep learning models such as U-Net, Transformer, hybrid deep learning, and Mamba-based methods in segmentation tasks.

Results:

Through the entire review process, It is found that most researchers preferred to use the Transformer-based U-Net model and mamba-based U-Net, especially the fusion model combination of U-Net and mamba, for image segmentation.