用3D修正流模型无训练引导，仅靠图像标签实现肺结节精准分割
Weakly-Supervised Lung Nodule Segmentation via Training-Free Guidance of 3D Rectified Flow
- 利用预训练3D修正流模型提供无训练指导信号
- 在LUNA16数据集上优于基线方法，小结节检测更稳定
- 无需重训练生成模型，适合临床快速部署
密集标注（如分割掩码）在3D医学图像中成本高昂，需专家逐体素标注。弱监督方法虽可缓解此问题，但依赖归因类方法，难以准确捕捉小结构如肺结节。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先进修正流与预测模型。该方法采用3D修正流模型的无训练引导，仅需对预测器进行图像级标签微调，无需重训练生成模型。实验表明，该方法在两个不同预测器上均提升了分割质量，能稳定检测不同大小和形状的肺结节。在LUNA16数据集上的结果优于基线方法，验证了生成基础模型在弱监督3D医学图像分割中的潜力。
原文摘要 · Abstract (English)
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.
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