Scholarlink

<p>The generated lung nodule data plays an indispensable role in the development of intelligent assisted diagnosis of lung cancer. Existing generative models, primarily based on Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPM), have demonstrated effectiveness but also come with certain limitations: GANs often produce artifacts and unnatural boundaries, and due to dataset limitations, they struggle with irregular nodules. While DDPMs are capable of generating a diverse range of nodules, their inherent randomness and lack of control limit their applicability in tasks such as segmentation. To synthesize controllable shapes and details of lung nodules, in this study, we propose a unified model that combines GAN and DDPM. Guided by multi-confidence masks, our method can synthesize customized lung nodule images by adding spikes or dents to the input mask, allowing control over shape, size, and other medical image features. The model consists of two parts: (1) a Rough Lung Nodule Generator, based on GAN, which synthesizes rough lung nodules of specified sizes and shapes using a multi-confidence mask, and (2) a Lung Nodule Optimizer, based on DDPM, which refines the rough results from the first part to produce more authentic boundaries. We validate our method using the LIDC-IDRI dataset. Experimental results demonstrate that our unified model achieves the best FID score, and the synthetic lung nodules it generates can serve as a valuable supplement to training datasets for segmentation tasks. Our study presents a unified model that effectively combines GAN and DDPM to generate high-quality and customized lung nodule images. This approach addresses the limitations of existing models by leveraging the strengths of both techniques. Our code is available at <a href="https://github.com/UtaUtaUtaha/CMCMGN">https://github.com/UtaUtaUtaha/CMCMGN</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Lung nodule synthesis guided by customized multi-confidence masks

  • 期刊论文

  • 发表日期：

    2025年7月12日

查看全文
  • Huashan Chen,
  • Yongxu Liu,
  • Chen Liu,
  • Qiuli Wang,
  • Rongping Wang

摘要

The generated lung nodule data plays an indispensable role in the development of intelligent assisted diagnosis of lung cancer. Existing generative models, primarily based on Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPM), have demonstrated effectiveness but also come with certain limitations: GANs often produce artifacts and unnatural boundaries, and due to dataset limitations, they struggle with irregular nodules. While DDPMs are capable of generating a diverse range of nodules, their inherent randomness and lack of control limit their applicability in tasks such as segmentation. To synthesize controllable shapes and details of lung nodules, in this study, we propose a unified model that combines GAN and DDPM. Guided by multi-confidence masks, our method can synthesize customized lung nodule images by adding spikes or dents to the input mask, allowing control over shape, size, and other medical image features. The model consists of two parts: (1) a Rough Lung Nodule Generator, based on GAN, which synthesizes rough lung nodules of specified sizes and shapes using a multi-confidence mask, and (2) a Lung Nodule Optimizer, based on DDPM, which refines the rough results from the first part to produce more authentic boundaries. We validate our method using the LIDC-IDRI dataset. Experimental results demonstrate that our unified model achieves the best FID score, and the synthetic lung nodules it generates can serve as a valuable supplement to training datasets for segmentation tasks. Our study presents a unified model that effectively combines GAN and DDPM to generate high-quality and customized lung nodule images. This approach addresses the limitations of existing models by leveraging the strengths of both techniques. Our code is available at https://github.com/UtaUtaUtaha/CMCMGN.