# CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation

arXiv: https://arxiv.org/abs/1806.04051 · HTML: https://mlsanity.com/paper/1806.04051/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 30 of 34)

- [FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation](https://mlsanity.com/paper/2205.01840/) (2022)
- [Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation](https://mlsanity.com/paper/2104.06087/) (2021)
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- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection](https://mlsanity.com/paper/2207.09389/) (2022)
- [Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System](https://mlsanity.com/paper/2209.02934/) (2022)
- [Analyzing Tumors by Synthesis](https://mlsanity.com/paper/2409.06035/) (2024)
- [Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models](https://mlsanity.com/paper/2104.02107/) (2021)
- [Controllable cardiac synthesis via disentangled anatomy arithmetic](https://mlsanity.com/paper/2107.01748/) (2021)
- [This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation](https://mlsanity.com/paper/2302.02150/) (2023)
- [Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data](https://mlsanity.com/paper/2310.15098/) (2023)
- [Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images](https://mlsanity.com/paper/2405.17520/) (2024)
- [Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection](https://mlsanity.com/paper/2105.01828/) (2021)
- [CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis](https://mlsanity.com/paper/2106.10230/) (2021)
- [3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation](https://mlsanity.com/paper/2203.08965/) (2022)
- [Principled Ultrasound Data Augmentation for Classification of Standard Planes](https://mlsanity.com/paper/2103.07895/) (2021)
- [Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective](https://mlsanity.com/paper/2107.11882/) (2021)
- [CT-SGAN: Computed Tomography Synthesis GAN](https://mlsanity.com/paper/2110.09288/) (2021)
- [Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19](https://mlsanity.com/paper/2211.05548/) (2022)
- [LesionDiffusion: Towards Text-controlled General Lesion Synthesis](https://mlsanity.com/paper/2503.00741/) (2025)
- [TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging](https://mlsanity.com/paper/2210.17076/) (2022)
- [DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference](https://mlsanity.com/paper/2404.16474/) (2024)
- [Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT](https://mlsanity.com/paper/2110.05280/) (2021)
- [Multiscale Vector-Quantized Variational Autoencoder for Endoscopic Image Synthesis](https://mlsanity.com/paper/2511.19578/) (2025)
- [Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images](https://mlsanity.com/paper/2107.00418/) (2021)
- [GAN-based Medical Image Small Region Forgery Detection via a Two-Stage Cascade Framework](https://mlsanity.com/paper/2205.15170/) (2022)
- [Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation](https://mlsanity.com/paper/2206.09065/) (2022)
- [A dynamic interactive learning framework for automated 3D medical image segmentation](https://mlsanity.com/paper/2312.06072/) (2023)

## Abstract (arXiv)

Data availability plays a critical role for the performance of deep learning systems. This challenge is especially acute within the medical image domain, particularly when pathologies are involved, due to two factors: 1) limited number of cases, and 2) large variations in location, scale, and appearance. In this work, we investigate whether augmenting a dataset with artificially generated lung nodules can improve the robustness of the progressive holistically nested network (P-HNN) model for pathological lung segmentation of CT scans. To achieve this goal, we develop a 3D generative adversarial network (GAN) that effectively learns lung nodule property distributions in 3D space. In order to embed the nodules within their background context, we condition the GAN based on a volume of interest whose central part containing the nodule has been erased. To further improve realism and blending with the background, we propose a novel multi-mask reconstruction loss. We train our method on over 1000 nodules from the LIDC dataset. Qualitative results demonstrate the effectiveness of our method compared to the state-of-art. We then use our GAN to generate simulated training images where nodules lie on the lung border, which are cases where the published P-HNN model struggles. Qualitative and quantitative results demonstrate that armed with these simulated images, the P-HNN model learns to better segment lung regions under these challenging situations. As a result, our system provides a promising means to help overcome the data paucity that commonly afflicts medical imaging.