# Free-form tumor synthesis in computed tomography images via richer generative adversarial network

arXiv: https://arxiv.org/abs/2104.09701 · HTML: https://mlsanity.com/paper/2104.09701/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 29 of 29)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning](https://mlsanity.com/paper/1901.00212/) (2019)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [Medical Image Synthesis with Context-Aware Generative Adversarial Networks](https://mlsanity.com/paper/1612.05362/) (2016)
- [Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN](https://mlsanity.com/paper/1904.10247/) (2019)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation](https://mlsanity.com/paper/2004.06042/) (2020)
- [Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images](https://mlsanity.com/paper/1612.06341/) (2016)
- [Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation](https://mlsanity.com/paper/1805.09707/) (2018)
- [How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis](https://mlsanity.com/paper/1710.09762/) (2017)
- [CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation](https://mlsanity.com/paper/1806.04051/) (2018)
- [Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection](https://mlsanity.com/paper/1802.07846/) (2018)
- [Macro-Micro Adversarial Network for Human Parsing](https://mlsanity.com/paper/1807.08260/) (2018)
- [Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection](https://mlsanity.com/paper/1905.13456/) (2019)
- [Conditional Infilling GANs for Data Augmentation in Mammogram Classification](https://mlsanity.com/paper/1807.08093/) (2018)
- [Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis](https://mlsanity.com/paper/1906.05845/) (2019)
- [CT synthesis from MR images for orthopedic applications in the lower arm using a conditional generative adversarial network](https://mlsanity.com/paper/1901.08449/) (2019)

## Built on by (most cited first, 16 of 16)

- [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024)
- [Label-Free Liver Tumor Segmentation](https://mlsanity.com/paper/2303.14869/) (2023)
- [From Pixel to Cancer: Cellular Automata in Computed Tomography](https://mlsanity.com/paper/2403.06459/) (2024)
- [LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models](https://mlsanity.com/paper/2403.14066/) (2024)
- [FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis](https://mlsanity.com/paper/2406.01264/) (2024)
- [Synthetic Tumors Make AI Segment Tumors Better](https://mlsanity.com/paper/2210.14845/) (2022)
- [METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy](https://mlsanity.com/paper/2104.10993/) (2021)
- [LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation](https://mlsanity.com/paper/2308.09026/) (2023)
- [Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images](https://mlsanity.com/paper/2208.02135/) (2022)
- [Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation](https://mlsanity.com/paper/2407.19284/) (2024)
- [Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images](https://mlsanity.com/paper/2203.06431/) (2022)
- [CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentation](https://mlsanity.com/paper/2506.15549/) (2025)
- [LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features](https://mlsanity.com/paper/2408.14977/) (2024)
- [CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion](https://mlsanity.com/paper/2503.06919/) (2025)
- [Controllable Lung Nodule Synthesis via Histogram-Regularized Latent Diffusion Models](https://mlsanity.com/paper/2605.30631/) (2026)
- [Diffusion-Based Tumor Inpainting for Renal Segmentation under Clinical Data Scarcity](https://mlsanity.com/paper/2609.21698/) (2026)

## Abstract (arXiv)

The insufficiency of annotated medical imaging scans for cancer makes it challenging to train and validate data-hungry deep learning models in precision oncology. We propose a new richer generative adversarial network for free-form 3D tumor/lesion synthesis in computed tomography (CT) images. The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function. The RicherDG has dilated-gated convolution layers to enable tumor-painting and to enlarge perceptive fields; and it has a novel richer convolutional feature association branch to recover multi-scale convolutional features especially from uncertain boundaries between tumor and surrounding healthy tissues. The hybrid loss function, which consists of a diverse range of losses, is designed to aggregate complementary information to improve optimization. We perform a comprehensive evaluation of the synthesis results on a wide range of public CT image datasets covering the liver, kidney tumors, and lung nodules. The qualitative and quantitative evaluations and ablation study demonstrated improved synthesizing results over advanced tumor synthesis methods.