Medical image segmentation is a fundamental task in computer-aided diagnosis, requiring models that balance segmentation accuracy and computational efficiency. However, existing segmentation models often struggle to effectively capture local and global contextual information, leading to boundary pixel loss and segmentation errors. In this paper, we propose U-CycleMLP, a novel U-shaped encoder-decoder network designed to enhance segmentation performance while maintaining a lightweight architecture. The encoder learns multiscale contextual features using position attention weight excitation blocks, dense atrous blocks, and downsampling operations, effectively capturing both local and global contextual information. The decoder reconstructs high-resolution segmentation masks through upsampling operations, dense atrous blocks, and feature fusion mechanisms, ensuring precise boundary delineation. To further refine segmentation predictions, channel CycleMLP blocks are incorporated into the decoder along the skip connections, enhancing feature integration while maintaining linear computational complexity relative to input size. Experimental results, both quantitative and qualitative, across three benchmark datasets demonstrate the competitive performance of U-CycleMLP in comparison with state-of-the-art methods, achieving better segmentation accuracy across all datasets, capturing fine-grained anatomical structures, and demonstrating robustness across different medical imaging modalities. Ablation studies further highlight the importance of the model's core architectural components in enhancing segmentation accuracy.

翻译：医学图像分割是计算机辅助诊断中的一项基础任务，要求模型在分割精度与计算效率之间取得平衡。然而，现有分割模型往往难以有效捕捉局部与全局上下文信息，导致边界像素丢失和分割误差。本文提出U-CycleMLP，一种新颖的U形编码器-解码器网络，旨在提升分割性能的同时保持轻量化架构。编码器通过位置注意力权重激励模块、密集空洞模块和下采样操作学习多尺度上下文特征，有效捕获局部与全局上下文信息。解码器通过上采样操作、密集空洞模块和特征融合机制重建高分辨率分割掩码，确保精确的边界描绘。为优化分割预测，通道CycleMLP模块通过跳跃连接整合至解码器中，在保持与输入尺寸线性计算复杂度的同时增强特征融合。在三个基准数据集上的定量与定性实验结果表明，U-CycleMLP相较于现有先进方法具有竞争优势，在所有数据集上均实现了更高的分割精度，能捕捉细粒度解剖结构，并在不同医学成像模态中表现出鲁棒性。消融实验进一步验证了模型核心架构组件对提升分割精度的重要性。