Despite the significant success achieved by deep learning methods in medical image segmentation, researchers still struggle in the computer-aided diagnosis of abdominal lymph nodes due to the complex abdominal environment, small and indistinguishable lesions, and limited annotated data. To address these problems, we present a pipeline that integrates the conditional diffusion model for lymph node generation and the nnU-Net model for lymph node segmentation to improve the segmentation performance of abdominal lymph nodes through synthesizing a diversity of realistic abdominal lymph node data. We propose LN-DDPM, a conditional denoising diffusion probabilistic model (DDPM) for lymph node (LN) generation. LN-DDPM utilizes lymph node masks and anatomical structure masks as model conditions. These conditions work in two conditioning mechanisms: global structure conditioning and local detail conditioning, to distinguish between lymph nodes and their surroundings and better capture lymph node characteristics. The obtained paired abdominal lymph node images and masks are used for the downstream segmentation task. Experimental results on the abdominal lymph node datasets demonstrate that LN-DDPM outperforms other generative methods in the abdominal lymph node image synthesis and better assists the downstream abdominal lymph node segmentation task.

翻译：尽管深度学习方法在医学图像分割领域取得了显著成功，但由于腹部环境复杂、病灶小且难以区分、标注数据有限，研究人员在腹部淋巴结的计算机辅助诊断中仍面临挑战。为解决这些问题，我们提出了一种结合条件扩散模型用于淋巴结生成与nnU-Net模型用于淋巴结分割的流程，通过合成多样化的真实腹部淋巴结数据来提升分割性能。我们提出了LN-DDPM——一种用于淋巴结生成的条件去噪扩散概率模型。该模型以淋巴结掩膜和解剖结构掩膜作为条件，通过全局结构条件与局部细节条件两种机制，区分淋巴结及其周围环境，更有效地捕捉淋巴结特征。由此获得的配对腹部淋巴结图像与掩膜被用于下游分割任务。在腹部淋巴结数据集上的实验结果表明，LN-DDPM在腹部淋巴结图像合成中优于其他生成方法，并能更好地辅助下游腹部淋巴结分割任务。