Grey Wolf optimized SwinUNet based transformer framework for liver segmentation from CT images.

• Manual liver segmentation from CT scans is error-prone and time-intensive, necessitating the need for automated methods. • A novel automated framework, GWO-SwinUNet combines Gray Wolf optimized Swin Transformer with UNet architecture for liver segmentation. • GWO-SwinUNet achieves superior segmentation accuracy with remarkable Dice and Jaccard coefficients. • The hybrid approach enhances fine-grained detail extraction while ensuring robustness and convergence speed. Liver segmentation in medical images, particularly CT scans, is crucial for diagnosing and treating liver diseases. However, manual segmentation by radiologists is time-consuming and prone to errors, necessitating automated solutions. Although deep learning methods have shown promise, issues such as model overfitting, convergence, and sensitivity to noise persist. In response, this paper introduces GWO-SwinUNet, a novel liver segmentation framework that combines the Gray Wolf optimized Swin Transformer with the UNet architecture. By leveraging this hybrid approach, the method enhances fine-grained details while maintaining robustness and convergence speed. Extensive experimentation on diverse datasets and comparisons with state-of-the-art methods demonstrate the superior performance of GWO-SwinUNet, achieving a Dice coefficient of 0.988 and a Jaccard coefficient of 0.979. A thorough ablation study further validates the efficacy of the proposed strategy. In summary, GWO-SwinUNet represents a significant advancement in liver segmentation, harnessing the strengths of transformer-based architectures and optimization techniques to enhance accuracy and efficiency in medical image analysis. [Display omitted] [ABSTRACT FROM AUTHOR]