Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation. However, success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and, furthermore, the resulting AI models being capable of detecting real tumors in images sourced from different domains (e.g., hospitals). This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation: early-stage tumors (< 2cm) tend to have similar imaging characteristics in computed tomography (CT), whether they originate in the liver, pancreas, or kidneys. We have ascertained that generative AI models, e.g., Diffusion Models, can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ. Moreover, we have shown that AI models trained on these synthetic tumors can be generalized to detect and segment real tumors from CT volumes, encompassing a broad spectrum of patient demographics, imaging protocols, and healthcare facilities.

翻译：肿瘤合成技术能够生成医学图像中的人工肿瘤，从而为肿瘤检测和分割的AI模型训练提供支持。然而，肿瘤合成的成功取决于能否合成视觉真实且跨器官通用的肿瘤，并进一步使训练得到的AI模型具备检测来自不同域（如不同医院）图像中真实肿瘤的能力。本文基于一项关键发现向通用化肿瘤合成迈进了一步：早期肿瘤（<2cm）在计算机断层扫描（CT）中无论源于肝脏、胰腺还是肾脏，其影像学特征均具有相似性。我们证实，即使仅使用单一器官的有限肿瘤样本进行训练，生成式AI模型（例如扩散模型）仍可合成适用于多种器官的逼真肿瘤。此外，研究表明基于这些合成肿瘤训练的AI模型能够泛化用于从CT影像中检测和分割真实肿瘤，涵盖广泛的患者人口统计学特征、成像协议及医疗机构场景。