用轻量化微调提升直肠癌MRI分割精度与可信度
Parameter-Efficient pretrained-CT-to-MRI Transfer for Rectal Cancer Segmentation: Performance-Calibration Trade-offs
- 基于预训练CT模型，采用肿瘤感知微调策略
- 参数减少70%仍保持高肿瘤检测率（93.9%）
- 多LoRA解码器集成实现最优不确定性校准
准确的直肠癌MRI分割对自适应放疗和肿瘤反应评估至关重要，但部署还需兼顾计算效率与可靠的不确定性估计。为此，我们提出SWIFT，一种基于Swin V2编码器的参数高效且肿瘤感知的微调方法，该编码器在10,444个公开3D CT数据上通过DINOv2风格目标预训练。通过四种累积配置：全微调（SWIFT）、解码器压缩（SWIFTe）、低秩适配（SWIFTe-LoRA）及四成员LoRA-解码器集成（SWIFTe-LDE4），将模型迁移至T2加权MRI。在单中心247例测试集（使用1.5或3 Tesla GE扫描仪）上评估几何精度、肿瘤检测、放射组学一致性与概率校准。相比SWIFT，SWIFTe参数减少70.1%（从72.8M降至21.8M），肿瘤检测率从89.9%提升至93.9%，表面DSC略降（0.61 vs 0.62），但放射组学一致性改善。移除肿瘤感知增强后检测率降至89.9%，表面DSC升至0.64，体现检测与边界一致性的权衡。SWIFTe-LoRA仅需SWIFTe 14.6%的可训练参数，性能相近。SWIFTe-LDE4经温度缩放后校准误差最低（期望校准误差0.217；Brier得分0.222），但绝对值仍显示残余校准偏差。使用公开的VoCo检查点也呈现相似效率-校准模式，表明结果稳健性源于预训练初始化而非外部临床泛化能力。
原文摘要 · Abstract (English)
Accurate rectal cancer segmentation from magnetic resonance imaging (MRI) is essential for adaptive radiotherapy and tumor response assessment, but deployment also requires computational efficiency and informative, calibrated uncertainty estimates. We therefore introduce SWIFT, a SWin pretrained model wIth parameter-eFficient and Tumor-aware fine-tuning for rectal cancer segmentation. A Swin V2 encoder pretrained on 10,444 public 3D CT volumes using a DINOv2-style objective was adapted to T2-weighted MRI through four cumulative configurations: full fine-tuning (SWIFT), decoder compression (SWIFTe), low-rank adaptation (SWIFTe-LoRA), and a four-member LoRA-decoder ensemble (SWIFTe-LDE4). Geometric accuracy, tumor detection, radiomic agreement, and probability calibration were evaluated on a held-out 247-case test set from a single-institution cohort acquired using 1.5 or 3 Tesla GE scanners. Compared with SWIFT, SWIFTe reduced total parameters by 70.1% (from 72.8M to 21.8M) and increased tumor detection rate from 89.9% to 93.9%, while achieving a slightly lower median surface DSC (0.61 versus 0.62) and improved radiomic agreement. In a separate SWIFTe ablation, removing tumor-aware augmentation reduced detection from 93.9% to 89.9% but increased surface DSC from 0.61 to 0.64, demonstrating a detection-boundary-agreement trade-off. SWIFTe-LoRA used 14.6% of SWIFTe's trainable parameters while retaining similar segmentation performance. SWIFTe-LDE4 achieved the lowest calibration errors among the four configurations after temperature scaling (expected calibration error, 0.217; Brier score, 0.222), although the absolute expected calibration error indicates residual miscalibration. Similar efficiency-calibration patterns were observed using the public VoCo checkpoint, supporting robustness across pretrained initializations rather than external clinical generalizability.
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