Semi-supervisedlearning has proven effective in the challenging field of 3d medical image segmentation. However, existing methods typically focus solely on utilizing 3d features, which limits their ability to capture...
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Semi-supervisedlearning has proven effective in the challenging field of 3d medical image segmentation. However, existing methods typically focus solely on utilizing 3d features, which limits their ability to capture local differences effectively. To address this limitation, we propose a dual Stream Fusion Model (dSFM) that fuses both 3d and2d features, enabling the model to capture both global and local information. The model consists of a Cross-dimensional Attention Fusion Module (CdAFM) and a Self-Adaptive Fusion Module (SAFM). The CdAFM integrates 3d and2d features using an attention mechanism, while the SAFM merges the segmentation results from both modalities through adaptive weighting. Together, these modules enhance the model's ability to focus on local differences while simultaneously learning global features. Specifically, the 3ddecoder receives fused features from both the 3d and2d encoders, and the final segmentation result is produced by combining the outputs of both the 3d and2ddecoders. Furthermore, we introduce an alignment loss to enforce consistency between the 3d and2d segmentation sub-networks through regularization. Comprehensive experiments on four publicly available 3d medical datasets demonstrate the effectiveness of our approach. Notably, on the BRaTS19 dataset, which contains only 20% annotated images, we observe a 2.83% improvement in the Jaccard index.
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