GCA-ResUNet:Image segmentation in medical images using grouped coordinate attention

TL;DR

GCA-ResUNet is an efficient medical image segmentation model that integrates grouped coordinate attention into ResNet-50, capturing global dependencies with minimal computational overhead, leading to superior accuracy on benchmark datasets.

Contribution

This paper introduces GCA-ResUNet, a novel segmentation network that combines grouped coordinate attention with ResNet-50, enhancing global context modeling in medical images efficiently.

Findings

Achieves 86.11% Dice on Synapse dataset

Reaches 92.64% Dice on ACDC dataset

Outperforms several state-of-the-art baselines

Abstract

Medical image segmentation underpins computer-aided diagnosis and therapy by supporting clinical diagnosis, preoperative planning, and disease monitoring. While U-Net style convolutional neural networks perform well due to their encoder-decoder structures with skip connections, they struggle to capture long-range dependencies. Transformer-based variants address global context but often require heavy computation and large training datasets. This paper proposes GCA-ResUNet, an efficient segmentation network that integrates Grouped Coordinate Attention (GCA) into ResNet-50 residual blocks. GCA uses grouped coordinate modeling to jointly encode global dependencies across channels and spatial locations, strengthening feature representation and boundary delineation while adding minimal parameter and FLOP overhead compared with self-attention. On the Synapse dataset, GCA-ResUNet achieves a Dice score of 86.11%, and on the ACDC dataset, it reaches 92.64%, surpassing several state-of-the-art baselines while maintaining fast inference and favorable computational efficiency. These results indicate that GCA offers a practical way to enhance convolutional architectures with global modeling capability, enabling high-accuracy and resource-efficient medical image segmentation.