An Adaptive Multi-Stage and Adjacent-Level Feature Integration Network for Brain Tumor Image Segmentation

The segmentation of brain tumor magnetic resonance imaging (MRI) plays a crucial role in assisting diagnosis, treatment planning, and disease progression evaluation. Convolutional neural networks (CNNs) and transformer-based methods have achieved significant progress due to their local and global feature extraction capabilities. However, similar to other medical image segmentation tasks, challenges remain in addressing issues such as blurred boundaries, small lesion volumes, and interwoven regions. General CNN and transformer approaches struggle to effectively resolve these issues. Therefore, a new multi-stage and adjacent-level feature integration network (MAI-Net) is introduced to overcome these challenges, thereby improving the overall segmentation accuracy. MAI-Net consists of dual-branch, multi-level structures and three innovative modules. The stage-level multi-scale feature extraction (SMFE) module focuses on capturing feature details from fine to coarse scales, improving detection of blurred edges and small lesions. The adjacent-level feature fusion (AFF) module facilitates information exchange across different levels, enhancing segmentation accuracy in complex regions as well as small volume lesions. Finally, the multi-stage feature fusion (MFF) module further integrates features from various levels to improve segmentation performance in complex regions. Extensive experiments on BraTS2020 and BraTS2021 datasets demonstrate that MAI-Net significantly outperforms existing methods in Dice and HD95 metrics. Furthermore, generalization experiments on a public ischemic stroke dataset confirm its robustness across different segmentation tasks. These results highlight the significant advantages of MAI-Net in addressing domain-specific challenges while maintaining strong generalization capabilities.