Four-dimensional computed tomography (4DCT) captures the full respiratory cycle of thoracic anatomy, yet current Internal Target Volume contouring workflows process each phase in isolation, discarding temporal coherence and leaving contours vulnerable to phase-specific artifacts. We present a lightweight framework that applies parameter-efficient fine-tuning to the Segment Anything Model 3 (SAM 3) via low-rank adaptation (LoRA) to align its text-prompted segmentation with the medical domain using only seven annotated 3D CT volumes. Furthermore, the framework incorporates a hard negative mining strategy to improve boundary discrimination in low-contrast thoracic regions. At inference, phase-wise predictions are refined through phase-coherent temporal filtering and spatial connectivity analysis. Since respiratory motion is continuous and periodic, genuine anatomy appears in contiguous blocks of phases, whereas transient artifacts appear sporadically and are thus effectively suppressed. Experiments on pulmonary and cardiac structures yield median Dice scores of 0.968 and 0.910 with 95th-percentile Hausdorff distances of 0.998 mm and 2.931 mm, respectively. The proposed framework effectively eliminates the severe false-positive predictions inherent in the zero-shot inference of the unadapted SAM 3. With only seven annotated volumes, the framework retains over 95% of full-data accuracy, and the entire pipeline is trainable on a single consumer-grade GPU, demonstrating a scalable, data-efficient solution for adaptive radiotherapy.

翻译：四维计算机断层扫描（4DCT）可捕捉胸腔解剖结构的完整呼吸周期，然而当前的内靶区勾画工作流程对每个时相独立处理，丢弃了时间一致性，使得轮廓易受时相特定伪影影响。我们提出一种轻量级框架，通过低秩适配（LoRA）对Segment Anything Model 3（SAM 3）进行参数高效微调，仅使用七组带标注的三维CT体数据即可将其文本提示分割能力与医学领域对齐。此外，该框架引入困难负样本挖掘策略以改善低对比度胸腔区域的边界判别能力。在推理阶段，通过时相一致的时间滤波和空间连通性分析对逐时相预测结果进行精炼。由于呼吸运动具有连续性和周期性，真实解剖结构会出现在连续的时相区块中，而瞬时伪影则零星出现并因此被有效抑制。在肺部和心脏结构上的实验分别获得0.968和0.910的中位Dice分数，以及0.998毫米和2.931毫米的95分位Hausdorff距离。所提框架有效消除了未适配SAM.3零样本推理中固有的严重假阳性预测。仅使用七组标注体数据，该框架保持了超过95%的全数据精度，且整个流程可在单块消费级GPU上训练，展示了自适应放疗中可扩展且数据高效的解决方案。