基于无监督指导的3D校正流肺结节分割
Weakly-Supervised Lung Nodule Segmentation via Training-Free Guidance of 3D Rectified Flow
AI总结：
本文提出一种无监督分割方法，结合预训练的校正流和预测器模型，通过无需训练的指导生成高质量肺结节分割结果，实验表明在LUNA16数据集上优于基线方法。
摘要：
密集标注如分割掩码在获得时成本高且耗时，尤其对于需要专家逐 voxel 标注的3D医学图像。弱监督方法旨在解决这一限制，但通常依赖于基于归因的方法，难以准确捕捉如肺结节等小结构。本文提出了一种弱监督分割方法，通过将预训练的最新校正流和预测器模型以即插即用的方式结合。我们的方法通过无需训练的指导方式训练3D校正流模型，仅需对预测器进行微调使用图像级标签，无需重新训练生成模型。所提出的方法为两个独立的预测器生成了改进的分割结果，一致检测不同大小和形状的肺结节。在LUNA16上的实验表明，该方法优于基线方法，突显了生成基础模型作为弱监督3D医学图像分割工具的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
