LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance

• URL: http://arxiv.org/abs/2510.18446v1
• Date: Tue, 21 Oct 2025 09:20:22 GMT
• Title: LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance
• Authors: Anna Oliveras, Roger Marí, Rafael Redondo, Oriol Guardià, Ana Tost, Bhalaji Nagarajan, Carolina Migliorelli, Vicent Ribas, Petia Radeva,
• Abstract summary: The method synthesizes images of size 256x256x256 at 1 mm isotropic resolution using a single mid-range GPU.<n>The conditioning masks delineate lung and nodule regions, enabling precise control over the output anatomical features.
• Score: 11.420298943913075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a new latent diffusion model to generate high-quality 3D chest CT scans conditioned on 3D anatomical masks. The method synthesizes volumetric images of size 256x256x256 at 1 mm isotropic resolution using a single mid-range GPU, significantly lowering the computational cost compared to existing approaches. The conditioning masks delineate lung and nodule regions, enabling precise control over the output anatomical features. Experimental results demonstrate that conditioning solely on nodule masks leads to anatomically incorrect outputs, highlighting the importance of incorporating global lung structure for accurate conditional synthesis. The proposed approach supports the generation of diverse CT volumes with and without lung nodules of varying attributes, providing a valuable tool for training AI models or healthcare professionals.

Related papers

• Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networks [3.735262910545115]
  We propose a two-stage generative adversarial network (TSGAN) to enhance the diversity and spatial controllability of synthetic data.<n>In the first stage, StyleGAN is used to generate semantic segmentation mask images, encoding lung nodules and tissue backgrounds.<n>The second stage uses the DL-Pix2Pix model to translate the mask map into CT images, employing local importance attention to capture local features.<n>Compared to the original dataset, the accuracy improved by 4.6% and mAP by 4% on the LUNA16 dataset.
  arXiv  Detail & Related papers  (2026-02-02T14:45:11Z)
• BridgeSplat: Bidirectionally Coupled CT and Non-Rigid Gaussian Splatting for Deformable Intraoperative Surgical Navigation [69.14180476971602]
  We introduce BridgeSplat, a novel approach for deformable surgical navigation.<n>Our method rigs 3D Gaussians to a CT mesh, enabling joint optimization of Gaussian parameters and mesh deformation.<n>We demonstrate BridgeSplat's effectiveness on visceral pig surgeries and synthetic data of a human liver under simulation.
  arXiv  Detail & Related papers  (2025-09-23T01:09:36Z)
• High-Fidelity 3D Lung CT Synthesis in ARDS Swine Models Using Score-Based 3D Residual Diffusion Models [13.79974752491887]
  Acute respiratory distress syndrome (ARDS) is a severe condition characterized by lung inflammation and respiratory failure, with a high mortality rate of approximately 40%. Traditional imaging methods, such as chest X-rays, provide only two-dimensional views, limiting their effectiveness in fully assessing lung pathology. This study synthesizes high-fidelity 3D lung CT from 2D generated X-ray images with associated physiological parameters using a score-based 3D residual diffusion model.
  arXiv  Detail & Related papers  (2024-09-26T18:22:34Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan [12.442169188451164]
  We propose an efficient end-to-end framework, the multi-encoder-based self-adaptive hard attention network (MESAHA-Net) for precise lung nodule segmentation in CT scans.<n>MESAHA-Net iteratively performs slice-by-slice 2D segmentation of lung nodules, focusing on the nodule region in each slice to generate 3D segmentation of lung nodules.<n>The proposed framework has been evaluated on the LIDC-IDRI dataset, the largest publicly available dataset for lung nodule segmentation.
  arXiv  Detail & Related papers  (2023-04-04T07:05:15Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection [52.93342510469636]
  Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation.
  arXiv  Detail & Related papers  (2022-07-19T16:38:48Z)
• Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection [6.693379403133435]
  Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life. Computer-aided detection/diagnosis (CAD) is proven valuable as a second or concurrent reader in this context. accurate detection of lung nodules remains a challenge for such CAD systems and even radiologists due to variability in size, location, and appearance of lung nodules. Motivated by recent computer vision techniques, here we present a self-supervised region-based 3D transformer model to identify lung nodules.
  arXiv  Detail & Related papers  (2022-04-30T01:19:00Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.