Precise delineation of anatomical and pathological structures within 3D medical volumes is crucial for accurate diagnosis, effective surgical planning, and longitudinal disease monitoring. Despite advancements in AI, clinically viable segmentation is often hindered by the scarcity of 3D annotations, patient-specific variability, data privacy concerns, and substantial computational overhead. In this work, we propose FALCON, a cross-domain few-shot segmentation framework that achieves high-precision 3D volume segmentation by processing data as 2D slices. The framework is first meta-trained on natural images to learn-to-learn generalizable segmentation priors, then transferred to the medical domain via adversarial fine-tuning and boundary-aware learning. Task-aware inference, conditioned on support cues, allows FALCON to adapt dynamically to patient-specific anatomical variations across slices. Experiments on four benchmarks demonstrate that FALCON consistently achieves the lowest Hausdorff Distance scores, indicating superior boundary accuracy while maintaining a Dice Similarity Coefficient comparable to the state-of-the-art models. Notably, these results are achieved with significantly less labeled data, no data augmentation, and substantially lower computational overhead.

翻译：在三维医学影像中精确描绘解剖结构与病理结构，对于准确诊断、有效手术规划及疾病长期监测至关重要。尽管人工智能技术不断进步，但临床可行的分割仍常受限于三维标注稀缺、患者特异性变异、数据隐私顾虑及巨大计算开销。本研究提出FALCON——一种跨域少样本分割框架，通过将数据作为二维切片处理来实现高精度三维体积分割。该框架首先在自然图像上进行元训练以学习可泛化的分割先验知识，随后通过对抗性微调与边界感知学习迁移至医学领域。基于支持线索的任务感知推理机制，使FALCON能够动态适应跨切片的患者特异性解剖变异。在四个基准数据集上的实验表明，FALCON始终取得最低的豪斯多夫距离分数，在保持与最先进模型相当的戴斯相似系数的同时，展现出更优的边界精度。值得注意的是，这些成果是在使用显著更少的标注数据、未进行数据增强且计算开销大幅降低的条件下实现的。