CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node
  Segmentation
Yongrui Yu Hanyu Chen Zitian Zhang Qiong Xiao Wenhui Lei Linrui Dai Yu Fu Hui Tan Guan Wang Peng Gao Xiaofan Zhang
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.