隐匿与寻找归因：基于弱监督的CT椎体转移瘤分割
Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT
AI总结：
本文提出一种基于椎体级别标签的弱监督分割方法，通过扩散自编码器和像素差异图识别可疑病变区域，利用隐匿与寻找归因机制在数据流形上投影并量化恶性贡献，实现高精度的椎体转移瘤分割。
摘要：
准确分割CT中的椎体转移瘤在临床中具有重要意义，但难以扩展，因为体素级标注稀缺，且溶骨性和成骨性病变常与良性退行性变化相似。我们介绍了一种基于椎体级别健康/恶性标签的2D弱监督方法，无需任何病变掩膜。该方法结合了生成扩散自编码器（DAE），该编码器对每个椎体生成分类器引导的健康编辑，以及像素级差异图，提出可疑候选病变。为了确定哪些区域真正反映恶性，我们引入隐匿与寻找归因：每个候选病变依次被揭示，而其他区域被隐藏，编辑后的图像通过DAE投影回数据流形，潜空间分类器量化该组件的孤立恶性贡献。高分区域形成最终的溶骨性或成骨性分割。在验证的放射科标注上，我们实现了强溶骨性/成骨性性能，尽管没有掩膜监督（F1: 0.91/0.85；Dice: 0.87/0.78），超过基线（F1: 0.79/0.67；Dice: 0.74/0.55）。这些结果表明，椎体级别标签可以转化为可靠的病变掩膜，证明了生成编辑结合选择性遮蔽支持在CT中的准确弱监督分割。
英文摘要：
Accurate segmentation of vertebral metastasis in CT is clinically important yet difficult to scale, as voxel-level annotations are scarce and both lytic and blastic lesions often resemble benign degenerative changes. We introduce a 2D weakly supervised method trained solely on vertebra-level healthy/malignant labels, without any lesion masks. The method combines a Diffusion Autoencoder (DAE) that produces a classifier-guided healthy edit of each vertebra with pixel-wise difference maps that propose suspect candidate lesions. To determine which regions truly reflect malignancy, we introduce Hide-and-Seek Attribution: each candidate is revealed in turn while all others are hidden, the edited image is projected back to the data manifold by the DAE, and a latent-space classifier quantifies the isolated malignant contribution of that component. High-scoring regions form the final lytic or blastic segmentation. On held-out radiologist annotations, we achieve strong blastic/lytic performance despite no mask supervision (F1: 0.91/0.85; Dice: 0.87/0.78), exceeding baselines (F1: 0.79/0.67; Dice: 0.74/0.55). These results show that vertebra-level labels can be transformed into reliable lesion masks, demonstrating that generative editing combined with selective occlusion supports accurate weakly supervised segmentation in CT.
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。
