The effective segmentation of the lesion region in gastric cancer images can assist physicians in diagnosing and reducing the probability of misdiagnosis. The U-Net has been proven to provide segmentation results comparable to specialists in medical image segmentation because of its ability to extract high-level semantic information. However, it has limitations in obtaining global contextual information. On the other hand, the Transformer excels at modeling explicit long-range relations but cannot capture low-level detail information. Hence, this paper proposes a Dual-Branch Hybrid Network based on the fusion Transformer and U-Net to overcome both limitations. We propose the Deep Feature Aggregation Decoder (DFA) by aggregating only the in-depth features to obtain salient lesion features for both branches and reduce the complexity of the model. Besides, we design a Feature Fusion (FF) module utilizing the multi-modal fusion mechanisms to interact with independent features of various modalities and the linear Hadamard product to fuse the feature information extracted from both branches. Finally, the Transformer loss, the U-Net loss, and the fused loss are compared to the ground truth label for joint training. Experimental results show that our proposed method has an IOU of 81.3%, a Dice coefficient of 89.5%, and an Accuracy of 94.0%. These metrics demonstrate that our model outperforms the existing models in obtaining high-quality segmentation results, which has excellent potential for clinical analysis and diagnosis. The code and implementation details are available at Github, https://github.com/ZYY01/DBH-Net/ .
© 2023. The Author(s).