Abstract
Medical image segmentation plays a crucial role in assisting clinical diagnosis, yet existing models often struggle with handling diverse and complex medical data, particularly when dealing with multi-scale organ and tissue structures. This paper proposes a novel medical image segmentation model, FE-SwinUper, designed to address these challenges by integrating the strengths of the Swin Transformer and UPerNet architectures. The objective is to enhance multi-scale feature extraction and improve the fusion of hierarchical organ and tissue representations through a feature enhancement Swin Transformer (FE-ST) backbone and an adaptive feature fusion (AFF) module. The FE-ST backbone utilizes self-attention mechanisms to efficiently extract rich spatial and contextual features across different scales, while the AFF module adapts to multi-scale feature fusion, mitigating the loss of contextual information. We evaluate the model on two publicly available medical image segmentation datasets: Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset. Our results show that FE-SwinUper outperforms existing state-of-the-art models in terms of Dice coefficient, pixel accuracy, and Hausdorff distance. The model achieves a Dice score of 91.58% on the Synapse dataset and 90.15% on the ACDC dataset. These results demonstrate the robustness and efficiency of the proposed model, indicating its potential for real-world clinical applications.