Dense annotations, such as segmentation masks, are expensive and time-consuming to obtain, especially for 3D medical images where expert voxel-wise labeling is required. Weakly supervised approaches aim to address this limitation, but often rely on attribution-based methods that struggle to accurately capture small structures such as lung nodules. In this paper, we propose a weakly-supervised segmentation method for lung nodules by combining pretrained state-of-the-art rectified flow and predictor models in a plug-and-play manner. Our approach uses training-free guidance of a 3D rectified flow model, requiring only fine-tuning of the predictor using image-level labels and no retraining of the generative model. The proposed method produces improved-quality segmentations for two separate predictors, consistently detecting lung nodules of varying size and shapes. Experiments on LUNA16 demonstrate improvements over baseline methods, highlighting the potential of generative foundation models as tools for weakly supervised 3D medical image segmentation.

翻译：密集标注（如分割掩膜）的获取成本高昂且耗时，尤其在需要逐体素专家标注的三维医学影像领域。弱监督方法旨在解决这一局限，但通常依赖基于归因的方法，难以精确捕捉肺结节等微小结构。本文提出一种弱监督肺结节分割方法，通过即插即用方式结合预训练的最优修正流模型与预测器。该方法采用免训练引导策略驱动三维修正流模型，仅需利用图像级标签微调预测器，无需重新训练生成模型。所提方法能在两个独立预测器上生成质量更优的分割结果，稳定检测不同尺寸与形态的肺结节。在LUNA16数据集上的实验表明，该方法优于基线方案，彰显了生成式基础模型作为弱监督三维医学图像分割工具的潜力。