Parameter-efficient fine-tuning and few-shot learning of multiscale vision transformers for liver tumour segmentation in abdominal CT scans

Effective Parameter-Efficient Fine-Tuning (PEFT) of segmentation models for computed tomography (CT) scans is crucial for liver tumour treatment planning, including preoperative volumetrics and radioembolization dose calculations. This study integrates Low-Rank Adaptation (LoRA) into the Swin UNETR, the state-of-the-art (SOTA) abdominal segmentation model, to enhance fine-tuning efficiency with minimal CT data for liver and tumour segmentation. Evaluating various LoRA ranks and sample sizes, we found that a rank of 8 achieved optimal performance, improving the Dice score by 15% and reducing the 95th percentile Hausdorff distance (HD95) by 66% compared to traditional fine-tuning, which fine-tunes the full model's parameters. Our LoRA-enhanced model demonstrates high adaptability with limited data through a few-shot learning (FSL) approach, providing a robust solution for efficient fine-tuning of deep learning (DL) segmentation models in clinical settings.