  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.
