Bridging spatial awareness and global context in medical image segmentation

TL;DR

This paper introduces U-CycleMLP, a lightweight U-shaped network that effectively captures local and global context for improved medical image segmentation, balancing accuracy and efficiency.

Contribution

The paper presents U-CycleMLP, a novel encoder-decoder architecture with position attention, dense atrous, and channel CycleMLP blocks for enhanced segmentation performance.

Findings

Outperforms state-of-the-art methods on three benchmark datasets.

Achieves higher segmentation accuracy and better boundary delineation.

Demonstrates robustness across different medical imaging modalities.

Abstract

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.