CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation

2024-03-26

arXiv.org

AI Summary

To address the challenges of small lesion size, complex anatomical structures, and scarce annotated data in abdominal lymph node segmentation, this paper proposes LN-DDPM—a conditional diffusion model incorporating dual conditioning on global anatomical structure and local details—within a generation-segmentation joint pipeline: high-fidelity paired CT images and masks are first synthesized, then fed into nnU-Net for segmentation. Key innovations include multi-scale mask guidance and explicit anatomical prior embedding, enabling precise decoupled modeling of lymph nodes and surrounding tissues. On an abdominal lymph node dataset, LN-DDPM achieves significantly superior synthetic image quality over GAN- and VAE-based baselines. Downstream segmentation yields a 4.2% Dice score improvement and an 18.7% increase in small-object detection rate, demonstrating synergistic gains between generative fidelity and segmentation performance.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Improves abdominal lymph node segmentation via synthetic data generation
• Addresses limited annotated data and complex abdominal environment challenges
• Enhances segmentation accuracy using conditional diffusion models and nnU-Net

Innovation

• Conditional diffusion model synthesizes abdominal lymph node images
• LN-DDPM uses masks for global and local conditioning
• Generated data improves nnU-Net segmentation performance