用文本控制生成3D CT病灶，提升分割效果并泛化到未见病灶类型。
LesionDiffusion: Towards Text-controlled General Lesion Synthesis
- 通过文本描述控制病灶生成，结合结构化报告模板实现精细调控。
- 在14类病灶、8个器官上训练，生成病灶与对应掩码，显著提升分割性能。
- 支持未见过的病灶类型和器官，适合医学图像合成与模型训练场景。
医学影像中的全监督病灶识别依赖大规模标注数据，但标注成本高且难获取。为此，合成病灶生成成为有前景的解决方案。然而，现有模型在可扩展性、病灶属性细粒度控制及复杂结构生成方面仍存在不足。本文提出LesionDiffusion，一种面向3D CT成像的文本可控病灶合成框架，可同时生成病灶及其对应掩码。通过使用结构化病灶报告模板，模型实现了对病灶属性的更强控制，并支持更广泛的病灶类型。我们构建了一个包含1,505个标注CT扫描的数据集，涵盖8个器官上的14类病灶，每例均配有病灶掩码与结构化报告。LesionDiffusion由两个组件构成：病灶掩码生成网络（LMNet）和病灶修复网络（LINet），均受病灶属性与图像特征引导。大量实验表明，该方法显著提升分割性能，在未见病灶类型和器官上具有良好泛化能力，优于当前最先进模型。代码已开源。
原文摘要 · Abstract (English)
Fully-supervised lesion recognition methods in medical imaging face challenges due to the reliance on large annotated datasets, which are expensive and difficult to collect. To address this, synthetic lesion generation has become a promising approach. However, existing models struggle with scalability, fine-grained control over lesion attributes, and the generation of complex structures. We propose LesionDiffusion, a text-controllable lesion synthesis framework for 3D CT imaging that generates both lesions and corresponding masks. By utilizing a structured lesion report template, our model provides greater control over lesion attributes and supports a wider variety of lesion types. We introduce a dataset of 1,505 annotated CT scans with paired lesion masks and structured reports, covering 14 lesion types across 8 organs. LesionDiffusion consists of two components: a lesion mask synthesis network (LMNet) and a lesion inpainting network (LINet), both guided by lesion attributes and image features. Extensive experiments demonstrate that LesionDiffusion significantly improves segmentation performance, with strong generalization to unseen lesion types and organs, outperforming current state-of-the-art models. Code is available at https://github.com/HengruiTianSJTU/LesionDiffusion.
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