LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features

TL;DR

This paper presents a novel SDF-based synthetic data generation method for rectal lymph node segmentation, improving accuracy and addressing data scarcity in rectal cancer diagnosis.

Contribution

Introduces an implicit SDF-based technique for generating realistic synthetic rectal lymph node masks to enhance segmentation models.

Findings

Synthetic data improves segmentation performance

Method produces continuous and morphologically diverse masks

Diffusion model effectively synthesizes complex lesions

Abstract

Accurate segmentation of rectal lymph nodes is crucial for the staging and treatment planning of rectal cancer. However, the complexity of the surrounding anatomical structures and the scarcity of annotated data pose significant challenges. This study introduces a novel lymph node synthesis technique aimed at generating diverse and realistic synthetic rectal lymph node samples to mitigate the reliance on manual annotation. Unlike direct diffusion methods, which often produce masks that are discontinuous and of suboptimal quality, our approach leverages an implicit SDF-based method for mask generation, ensuring the production of continuous, stable, and morphologically diverse masks. Experimental results demonstrate that our synthetic data significantly improves segmentation performance. Our work highlights the potential of diffusion model for accurately synthesizing structurally complex lesions, such as lymph nodes in rectal cancer, alleviating the challenge of limited annotated data in this field and aiding in advancements in rectal cancer diagnosis and treatment.