Medical image segmentation refers to the process of accurately separating the regions of interest in medical images from the background to extract key information，such as organs and lesion areas，and provide support for subsequent diagnosis，analysis，evaluation，and treatment. Benefiting from the widespread application of deep learning，especially convolutional neural networks and Transformer frameworks，it has evolved from using traditional manual methods to automated and universal intelligent segmentation. Many AI-assisted diagnosis and treatment systems are used in practice. Compared with simple pixel computing，existing network structures can recognize various biological information well by extracting and classifying image features，and automated segmentation has achieved substantial progress. However，challenging issues，such as how to solve the difficulty of edge acquisition，how to fully utilize the obtained features，and how to determine which features among multiple ones play a more important role in segmentation judgment，remain. With the deepening of the network layer，the loss of high-frequency detail information leads to blurred and diffused image edges. Reasonable utilization and accurate fusion of multilevel and multiscale feature information directly affect the accuracy of image segmentation. To address these issues，we introduce three modules and propose a novel edge-guided multiperception decoder for medical image segmentation （EMPD）. The three modules are edge information perception module，dual-scale cascade gating，and three-dimensional（3D） attention fusion module.

Using EMPD as a decoder after some high-performance encoders can effectively improve the model’s segmentation accuracy. The input image first enters the three-dimensional（3D） attention fusion module，which utilizes the encoder input features to interactively fuse positional and channel features. By rearranging the attention mechanism at the pixel location，features from the position，channel，and pixel dimensions are interactively combined to generate a three-dimensional（3D） attention map. This attention map is then applied to the input to extract features at multiple scales for feature enrichment. These features are interactively combined with encoder information from the previous layer through two-scale cascade gating. In this part，we simultaneously extract and interactively merge information from different scales and layers to highlight primary information and suppress secondary information. An edge-aware module processes the original image and performs corresponding downsampling and edge extraction operations at different scales. During the progressive upsampling process，multiple operators extract edge information from different perspectives. This combination of multiple edge information，coupled with the introduction of learnable variables，makes our operator more informative，more learnable，and less expensive than others. The final learned result is inputted into the next layer of three-dimensional（3D） attention fusion module，and the multilayer predicted image is combined with the MUTATION loss to obtain the final segmentation result.

This study conducted experimental evaluations on seven datasets representing three medical image segmentation tasks. The datasets are ClincDB，ColonDB，CVC-300，Kvasir，and EITS datasets for single-class polyp segmentation； the ISIC2017 dataset for single-class skin lesion segmentation； and the Synapse dataset for multiorgan segmentation. The proposed method achieved a 1.58% improvement in segmentation performance on the ClinicDB dataset compared with state-of-the-art methods. On the five Polyp and ISIC2017 datasets，the Dice score improved by an average of 0.77% compared with the best-performing network. On the Synapse dataset，the Dice score and mean intersection over union improved by 2.16% and 3.81%，respectively，compared with PVT Cascade. In the segmentation of the left and right kidneys，the Dice score improved by 2.92% and 2.50%，respectively. In the large stomach region，the proposed method achieved a 3.28% improvement compared with PVT Cascade. Ablation experiments conducted on this architecture demonstrated that on multiple datasets，the Dice coefficient improved by more than 3 percentage points compared with a simple encoder cascade architecture. Introducing edge information and combining it with an effective fusion strategy substantially improved the problem of poor edge fitting and enhanced the overall segmentation accuracy，proving the effectiveness of the proposed module. However，having abundant edge information is not always good. The number and distribution of edge-aware modules need to be controlled. If the image depth is too large，a small number of learnable parameters may be insufficient to compensate for the conflict between semantic and edge information，potentially leading to a decrease in accuracy.

EMPD，combined with a high-performance encoder，achieved optimal performance on the five datasets，and its results approached the current best results on the two remaining datasets. Its performance surpassed that of mainstream image segmentation methods，and it demonstrated good generalization properties. This work provides new tools and model frameworks for medical image segmentation research，thus helping achieve effective medical-assisted diagnosis.