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.
Oliveras et al. (Tue,) studied this question.