计算机科学与探索2026，Vol.20Issue(9)：2655-2677,23.DOI:10.3778/j.issn.1673-9418.2506031
DynaSynergic-UNet:面向医学图像分割的动态协同UNet
DynaSynergic-UNet:Dynamic Collaborative UNet for Medical Image Segmentation
摘要
Abstract
The accuracy of medical image segmentation is crucial for disease diagnosis and treatment planning,yet existing methods still have limitations in multi-scale feature perception optimization,cross-level feature fusion alignment,and synergy between edge extraction and overall segmentation tasks.To address the above challenges,a dynamic cooperative segmentation network,DynaSynergic-UNet,is proposed.DynaSynergic-UNet consists of the deformable attention trans-former++(DAT++),the multi-scale large kernel channel-spatial attention(MLCSA)module,the bidirectional deformable alignment cooperative attention fusion(BiDCAF)module,and the edge guided multi-scale refinement(EGMR)module in concert.Among them,the DAT++encoder is used to encode the spatial structural feature representation of the input image;the MLCSA module captures the local details through the multi-scale parallel large kernel depth wise convolution with the ability of global sensing at the same time,and it enhances the focusing and discriminative perception of important target regions through channel attention and spatial attention;the BiDCAF module performs dynamic spatial feature alignment through the deformable mechanism and combines gated fusion,cross-layer cooperative attention calibration,and feature cross-complementarity to solve the encoder-decoder spatial feature representation difference and feature semantic gap problem;and the EGMR module performs edge fine-grained extraction through the multi-scale target region cooperative Sobel operator,which promotes the consistency improvement between edge accuracy and target region,and introduces boundary difference over union loss(Boundary DoU Loss)to optimize the effect of edge-guided segmentation.The pro-posed method is experimentally compared with recent advanced models such as EMCAD,AgileFormer,D-LKA Net,Swin-UNet,etc.On the public medical image segmentation datasets—the Synapse abdominal multi-organ CT dataset,the ACDC cardiac MRI dataset,and the ISIC2018 dermatology dataset,the small version of the model achieves average Dice similarity coefficients of 85.29%,92.33%,and 89.44%with 6.064×107 parameters,the tiny version achieves average Dice similarity coefficients of 84.49%,92.11%,and 89.19%with 2.724×107 parameters.The proposed model has a better trade-off among segmentation performance,resource utilization,and inference speed.The proposed model has also conducted cross-domain fine-tuning experiments using the ISIC2018 dermatology dataset and the LiTS2017 liver tumor dataset to further explore its generalization capabilities.DynaSynergic-UNet provides an efficient and robust dynamic synergistic solution for medical image segmentation,which is valuable for precise clinical diagnosis and treatment.关键词
医学图像分割/多尺度特征交互/动态可变形对齐/特征融合/边缘引导细化Key words
medical image segmentation/multi-scale feature interaction/dynamic deformable alignment/feature fusion/edge-guided refinement分类
信息技术与安全科学引用本文复制引用
邱云飞,李波澄..DynaSynergic-UNet:面向医学图像分割的动态协同UNet[J].计算机科学与探索,2026,20(9):2655-2677,23.基金项目
国家自然科学基金(62173171) （62173171）
辽宁省教育厅基本科研项目(LJ212410147079). This work was supported by the National Natural Science Foundation of China(62173171),and the Basic Research Project of Educa-tional Department of Liaoning Province(LJ212410147079). （LJ212410147079）
