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.

翻译：医学影像中的全监督病灶识别方法因依赖大规模标注数据集而面临挑战，这些数据集成本高昂且难以收集。为解决这一问题，合成病灶生成已成为一种前景广阔的方法。然而，现有模型在可扩展性、病灶属性的细粒度控制以及复杂结构生成方面存在不足。我们提出了LesionDiffusion，一种用于三维CT成像的文本可控病灶合成框架，能够同时生成病灶及其对应的掩码。通过利用结构化的病灶报告模板，我们的模型提供了对病灶属性更强的控制能力，并支持更广泛的病灶类型。我们引入了一个包含1,505个标注CT扫描的数据集，其中包含配对的病灶掩码和结构化报告，涵盖8个器官的14种病灶类型。LesionDiffusion由两个组件构成：病灶掩码合成网络（LMNet）和病灶修复网络（LINet），两者均在病灶属性和图像特征的引导下工作。大量实验表明，LesionDiffusion显著提升了分割性能，对未见过的病灶类型和器官表现出强大的泛化能力，其性能优于当前最先进的模型。代码可在https://github.com/HengruiTianSJTU/LesionDiffusion获取。