Transformer-based architectures have significantly improved 3D medical image segmentation, yet their high computational demands hinder real-time clinical applications. This study addresses this issue by optimising 3D SwinUNETR, a state-of-the-art transformer model, reducing the number of layers while preserving segmentation accuracy. The goal is to develop a lightweight model capable of real-time liver segmentation from computed tomography (CT) scans, addressing the constraints of resource-limited clinical environments. We propose SwinUNETR-36, a reduced-depth variant of SwinUNETR, and compare it against the original SwinUNETR-48 and a more compact SwinUNETR-24. The models are evaluated on the BTCV dataset, using Dice Similarity Coefficient (DSC), Normalised Surface Distance (NSD), Mean Average Surface Distance (MASD), Hausdorff Distance (HD), and Relative Volume Difference (RVD) as performance measurements. Results demonstrate that SwinUNETR-36 achieves a 50% reduction in computational complexity compared to SwinUNETR-48 while maintaining comparable segmentation accuracy. Additionally, qualitative assessments reveal that SwinUNETR-36 provides a closer volumetric match to the ground truth, avoiding the over-segmentation errors observed in SwinUNETR-48 and the errors present in SwinUNETR-24. These findings confirm that reducing transformer depth effectively balances segmentation accuracy and computational efficiency, making deep learning models more viable for real-time clinical deployment. Future research will explore additional model compression techniques, such as pruning and quantisation, to enhance performance on limited systems.