GCtx-UNet: Efficient Network for Medical Image Segmentation

Automated medical image segmentation is critical in providing valuable information for the prevention, diagnosis, progression monitoring, and prognosis of various diseases, as well as quantitative pathology assessment. The U-shaped deep-neural networks, which include encoders, decoders, and skip connections, are now the most widely used methods for medical image segmentation. Although the U-shaped networks have achieved state-of-the-art performance in numerous medical image segmentation tasks, it still has limitations. One primary limitation is the encoders' ability to effectively extract and integrate long-range and local features. Methods based on Convolutional Neural Networks (CNNs) such as UNet [26] and UNet++ [35] excel at capturing local features, but they struggle to model long-range dependencies within data. While Transformer-based methods such as Swin-UNet [6] can model long-range pixel relations, they lack spatial induction bias in modeling local information, which leads to unsatisfactory results. Past research explored CNN-Transformer hybrid architectures such as TransUnet [8] to capture global and local information but these models often significantly increase the number of parameters.