GCtx-UNet: Efficient Network for Medical Image Segmentation
<|reference_start|>GCtx-UNet: Efficient Network for Medical Image Segmentation: Medical image segmentation is crucial for disease diagnosis and monitoring. Though effective, the current segmentation networks such as UNet struggle with capturing long-range features. More accurate models such as TransUNet, Swin-UNet, and CS-UNet have higher computation complexity. To address this problem, we propose GCtx-UNet, a lightweight segmentation architecture that can capture global and local image features with accuracy better or comparable to the state-of-the-art approaches. GCtx-UNet uses vision transformer that leverages global context self-attention modules joined with local self-attention to model long and short range spatial dependencies. GCtx-UNet is evaluated on the Synapse multi-organ abdominal CT dataset, the ACDC cardiac MRI dataset, and several polyp segmentation datasets. In terms of Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) metrics, GCtx-UNet outperformed CNN-based and Transformer-based approaches, with notable gains in the segmentation of complex and small anatomical structures. Moreover, GCtx-UNet is much more efficient than the state-of-the-art approaches with smaller model size, lower computation workload, and faster training and inference speed, making it a practical choice for clinical applications.<|reference_end|>
@article{alrfou2024gctx-unet:,
    title={GCtx-UNet: Efficient Network for Medical Image Segmentation},
    author={Khaled Alrfou, Tian Zhao},
    journal={arXiv preprint arXiv:2406.05891},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05891},
    primaryClass={eess.IV cs.CV cs.LG}
}