# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
###### Abstract  
Hybrid CNN-Transformer networks seek to merge the local feature extraction capabilities of CNNs with the long-range dependency modeling abilities of Transformers, aiming to simultaneously address both local details and global contextual information. However, in many existing studies, CNNs and Transformers are often combined through the straightforward fusion of encoder features, which does not promote effective interaction between the two, thus limiting the potential benefits of each architecture. To overcome this shortfall, this study introduces a novel medical image segmentation (MIS) network, designated as DEFI-Net, which is based on dual-encoder interactive fusion. This network enhances segmentation performance by fostering interactive learning and feature fusion between the CNN and Transformer encoders. Specifically, during the encoding phase, DEFI-Net utilizes parallel encoding with both the CNN and Transformer to extract local and global features from the input images. The global-local interaction learning (GLIL) module then enables both the Transformer and CNN to assimilate global semantics and local details from each other, fully leveraging the strengths of the two encoders. In the feature fusion phase, the global-local feature fusion (GLFF) module integrates features from both encoders, using both global and local information to produce a more precise and comprehensive representation of features. Extensive experiments conducted on multiple public datasets, including multi-organ, cardiac, and colon polyp datasets, demonstrate that DEFI-Net surpasses several existing methods in terms of segmentation accuracy, thus highlighting its effectiveness and robustness in MIS tasks. medical image segmentation; image segmentation; deep learning; hybrid CNN-Transformer model Article  
10 February 2025  
Revised: 27 March 2025  
Accepted: 27 March 2025  
Submitted: 30 March 2025  
**Citation:** [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]. Medical image segmentation (MIS) is crucial in aiding physicians to precisely ascertain the location, size, and shape of lesions, and it provides a robust foundation for accurate medical diagnostics and treatment planning. Medical images, unlike natural images, pose unique challenges such as intricate organ details, indistinct lesion boundaries, and low contrast. Therefore, achieving efficient and accurate segmentation is of critical importance. With the rapid advancements in deep learning technology, convolutional neural networks (CNNs) have become the most prevalent approach for MIS tasks. [PERSON] et al. [1] introduced U-Net, a segmentation method based on a fully convolutional network that features a symmetric encoder-decoder structure with skip connections to preserve detailed information. The U-Net architecture has shown superior segmentation performance and its symmetric design has been widely adopted in various medical segmentation applications. Subsequent research has further refined U-Net to address diverse challenges in medicalsegmentation, yielding variants such as V-Net [2], attention U-Net [3], HADCNet [4], and IBU-Net [5]. However, the limited receptive field of convolutional kernels in CNNs allows for the excellent capture of local details but poses difficulties in modeling global contextual information, which limits the overall effectiveness of these segmentation methods [6; 7; 8; 9; 10]. Vision Transformers (ViTs) [11] have shown promise in enhancing the capability of networks to model long-range dependencies via self-attention mechanisms, effectively extracting global contextual information [12; 13]. This has led to the development of improved methods based on ViTs, such as DeiT [14], PVT [15], and Swin-Transformer [16], which have been successfully applied in various vision-related tasks, demonstrating the efficacy of Transformers in visual applications. Inspired by these developments, several innovative approaches have integrated Transformers into networks specifically for MIS.