GCA-ResUNet:Image segmentation in medical images using grouped coordinate attention
This work addresses the need for efficient and accurate segmentation in medical imaging for clinical applications, representing an incremental improvement over existing methods.
The paper tackled the problem of capturing long-range dependencies in medical image segmentation by proposing GCA-ResUNet, which integrates Grouped Coordinate Attention into ResNet-50, achieving Dice scores of 86.11% on the Synapse dataset and 92.64% on the ACDC dataset, surpassing state-of-the-art baselines.
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.