# LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features

arXiv: https://arxiv.org/abs/2408.14977 · HTML: https://mlsanity.com/paper/2408.14977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 21 of 21)

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- [Mask-conditioned latent diffusion for generating gastrointestinal polyp images](https://mlsanity.com/paper/2304.05233/) (2023)
- [LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models](https://mlsanity.com/paper/2403.14066/) (2024)
- [Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation](https://mlsanity.com/paper/2402.04031/) (2024)
- [CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation](https://mlsanity.com/paper/2308.08283/) (2023)
- [Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation](https://mlsanity.com/paper/2404.08916/) (2024)

## Abstract (arXiv)

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.