Accurate segmentation of liver structures in CT images is crucial for various medical applications, including disease diagnosis and treatment planning. Leveraging deep learning techniques, particularly 3DResUNet architecture and DenseCRF post-processing, offers promising avenues for achieving high-quality liver segmentation. However, challenges such as dataset variability, computational complexity, and optimization of model parameters need to be addressed to ensure robust performance.