TriFusion-EdgePVT:融合空间通道多尺度特征与边缘增强的医学图像分割方法OA

To address the limitation of convolutional neural network(CNN)in capturing long-range dependencies due to their local receptive fields,this study incorporated a PVTv2 Transformer encoder for powerful global context modeling.Furthermore,to mitigate the Transformer's weaknesses in local context understanding and detail perception,the study designed three novel modules:the residual gated cross-attention module(RGCAM)enhanced activation of relevant features and suppressed irrele-vant information to strengthen both long-range and local contexts;the multi-kernel parallel convolution attention module(MKPCAM)fused spatial-channel attention with multi-scale convolutions to synergistically boost global dependency modeling;the cascaded edge-enhanced upsampling module(CEEUM)hierarchically processed multi-scale edge information to improve detail perception.Experimental results on the Synapse dataset demonstrated that TriFusion-Edge PVT achieved a DSC of 83.61％and HD95 of 15.13.The model also outperformed existing state-of-the-art methods across seven binary medical data-sets.These findings validate the model's comprehensive advantages in global modeling,local semantic preservation,and de-tail recovery,and showcase its cross-dataset generalization capabilities.