Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT

Abstract

State-of-the-art interactive segmentation with foundation models remains constrained by compute and the need for annotations. We evaluated parameter-efficient fine-tuning (PEFT) of the Segment Anything Model (SAM) by inserting baseline adapters, Low-Rank Adaptation (LoRA), 4-bit Quantized Low-Rank Adaptation (QLoRA), and a convolutional adapter (Conv-Adapter), as well as our proposed Directional Spectral Top-K adapter (DiSCo), training only the adapters while keeping the SAM backbone frozen. DiSCo performs singular value decomposition of row-normalized weights to obtain spectral bases, then learns rank-gated spectral coefficients, per-output magnitude offsets, and a spectral gain; it supports optional Top-K rank selection at inference while keeping only 0.14 M parameters trainable. We benchmarked five prompting regimes, none, single-point, multi-point, and bounding boxes at an intersection over union (IoU) of 0.50 and 0.75, applied to abdominal CT scans of colorectal liver metastases, and reported segmentation metrics (Dice and the 95th-percentile Hausdorff distance (HD95)) and compute metrics (trainable parameters, latency, memory, throughput). Conv-Adapter and LoRA achieved the highest accuracy (overall Dice 0.793 and 0.792; single-point 0.795 and 0.792; HD95 32 mm). QLoRA was close (overall 0.766; single-point 0.768; HD95 36.41 mm) while offering the most favorable compute profile (0.91 M trainable parameters, 120 ms latency, 4.9 GB peak memory). DiSCo maximized parameter efficiency, achieving the highest Dice per million trainable parameters (4.66), showing an accuracy-efficiency trade-off (overall Dice 0.653; single-point 0.698; HD95 49.53 mm). These results indicate that PEFT on foundation models enables more accurate liver tumor segmentation with reduced adaptation costs, supporting rapid scanner-specific tuning, point- or box-prompt workflows, and broader deployability for preoperative volumetrics when compute and labeled data are limited.