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、病灶微小且难以区分以及标注数据有限，研究人员在腹部淋巴结的计算机辅助诊断方面仍面临挑战。为解决这些问题，本文提出了一种集成条件扩散模型进行淋巴结生成和nnU-Net模型进行淋巴结分割的流程，通过合成多样化的真实腹部淋巴结数据来提升腹部淋巴结的分割性能。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。LN-DDPM利用淋巴结掩码和解剖结构掩码作为模型条件，这些条件通过全局结构约束和局部细节约束两种机制协同作用，以区分淋巴结与其周围组织，并更好地捕捉淋巴结特征。获得的成对腹部淋巴结图像与掩码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方面优于其他生成方法，并能更有效地辅助下游腹部淋巴结分割任务。