超越前景：基于病灶引导的自适应黏膜上下文传播的视场感知息肉图像合成

中文摘要 AI 辅助

合成图像和掩膜对可以缓解结肠镜检查标注稀缺的问题，但逼真的合成需要在保留给定病灶的同时生成兼容的黏膜。现有的前景引导方法将所有非前景像素视为背景，并主要依赖局部整合。直接将它们应用于结肠镜检查会导致两个问题：非黏膜的黑色区域污染生成的组织，以及局部推理产生不一致的黏膜纹理和光照。我们提出LAMP，这是第一个基于病灶引导的自适应黏膜上下文传播的前景引导息肉图像合成框架。LAMP使用视场（FOV）掩膜明确区分病灶、有效黏膜和相机外部区域。病灶到黏膜的交叉注意力提取病灶外观条件用于有效黏膜位置，而视场约束的多方向视觉感受野加权键值（Vision Receptance Weighted Key Value）在合法的组织支持区域上传播这些条件。随后，一个自适应门控制它们与扩散U-Net的残差融合。在五个息肉数据集上的大量实验表明，LAMP在整体生成质量上大幅优于现有方法，并持续提升五个下游分割模型的性能。我们的代码将在该https URL发布。

英文摘要

Synthetic image and mask pairs can alleviate scarce colonoscopy annotations, but realistic synthesis requires preserving the supplied lesion while generating compatible mucosa. Existing foreground-guided methods treat all non-foreground pixels as background and rely mainly on local integration. Directly applying them to colonoscopy causes two problems: non-mucosal black regions contaminate generated tissue, and local reasoning produces inconsistent mucosal texture and illumination. We propose LAMP, the first foreground-guided framework for polyp image synthesis based on lesion-guided adaptive mucosal context propagation. LAMP explicitly separates the lesion, valid mucosa, and camera exterior using a field-of-view (FOV) mask. Lesion-to-Mucosa cross-attention extracts lesion appearance conditions for valid-mucosa locations, while FOV-constrained multidirectional Vision Receptance Weighted Key Value propagates them over legal tissue support. An adaptive gate then controls their residual fusion into the diffusion U-Net. Extensive experiments on five polyp datasets demonstrate that LAMP substantially outperforms existing methods in overall generation quality and consistently improves five downstream segmentation models. Our code will be released at https://github.com/wangtong627/LAMP.

发表机构

• Southeast University（东南大学）
• MBZUAI（穆罕默德·本·扎耶德人工智能大学）
• Case Western Reserve University（凯斯西储大学）

机构由 AI 辅助整理，请以论文原文为准。