Scholarlink

<p>Accurate and automated analysis of chest Computed Tomography (CT) scans is critical for early detection and risk stratification of lung cancer, the leading cause of cancer-related mortality worldwide. However, the development of robust deep learning models for lung nodule analysis is hindered by the limited availability of large, diverse, and well-annotated 3D CT datasets. This work presents an anatomically guided latent diffusion framework for synthesizing high-quality three-dimensional chest CT volumes. The proposed approach, termed LAND (Lung and Nodule Diffusion), conditions the generative process on 3D anatomical masks of the lungs and pulmonary nodules to ensure accurate spatial localization and realistic anatomical structure. A dedicated variational autoencoder (VAE) encodes anatomical masks into a latent representation that preserves fine-grained nodule morphology. In addition, conditional texture modeling within masked nodule regions enables controlled variation in lesion appearance. Compared with existing 3D diffusion-based methods, LAND substantially reduces computational requirements and generates 256&#xa0;×&#xa0;256&#xa0;×&#xa0;256 volumes at 1 mm isotropic resolution using 10–16 GB of GPU memory during training and less than 8 GB during inference. Experimental results demonstrate high visual fidelity and anatomical realism, further supported by improved performance in downstream lung nodule segmentation and classification tasks. These findings indicate that LAND provides a practical and efficient framework for anatomically guided 3D medical image synthesis and data augmentation.</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

Anatomically guided latent diffusion for high-resolution 3D chest CT synthesis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

查看全文
  • Anna Oliveras,
  • Roger Marí,
  • Rafael Redondo,
  • Oriol Guardià,
  • Cynthia Ifeyinwa Ugwu,
  • Ana Tost,
  • Bhalaji Nagarajan,
  • Carolina Migliorelli,
  • Vicent Ribas,
  • Petia Radeva

摘要

Accurate and automated analysis of chest Computed Tomography (CT) scans is critical for early detection and risk stratification of lung cancer, the leading cause of cancer-related mortality worldwide. However, the development of robust deep learning models for lung nodule analysis is hindered by the limited availability of large, diverse, and well-annotated 3D CT datasets. This work presents an anatomically guided latent diffusion framework for synthesizing high-quality three-dimensional chest CT volumes. The proposed approach, termed LAND (Lung and Nodule Diffusion), conditions the generative process on 3D anatomical masks of the lungs and pulmonary nodules to ensure accurate spatial localization and realistic anatomical structure. A dedicated variational autoencoder (VAE) encodes anatomical masks into a latent representation that preserves fine-grained nodule morphology. In addition, conditional texture modeling within masked nodule regions enables controlled variation in lesion appearance. Compared with existing 3D diffusion-based methods, LAND substantially reduces computational requirements and generates 256 × 256 × 256 volumes at 1 mm isotropic resolution using 10–16 GB of GPU memory during training and less than 8 GB during inference. Experimental results demonstrate high visual fidelity and anatomical realism, further supported by improved performance in downstream lung nodule segmentation and classification tasks. These findings indicate that LAND provides a practical and efficient framework for anatomically guided 3D medical image synthesis and data augmentation.