# Vox2Vox: 3D-GAN for Brain Tumour Segmentation

arXiv: https://arxiv.org/abs/2003.13653 · HTML: https://mlsanity.com/paper/2003.13653/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

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

- [MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images](https://mlsanity.com/paper/2310.03559/) (2023)
- [3D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generation](https://mlsanity.com/paper/2412.13059/) (2024)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study](https://mlsanity.com/paper/2103.16262/) (2021)
- [Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task](https://mlsanity.com/paper/2201.03777/) (2022)
- [A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN](https://mlsanity.com/paper/2403.05384/) (2024)
- [HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images](https://mlsanity.com/paper/2103.16617/) (2021)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [Blaze3DM: Marry Triplane Representation with Diffusion for 3D Medical Inverse Problem Solving](https://mlsanity.com/paper/2405.15241/) (2024)
- [DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2507.00983/) (2025)
- [MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing](https://mlsanity.com/paper/2204.03197/) (2022)
- [Generative Adversarial Networks and Other Generative Models](https://mlsanity.com/paper/2207.03887/) (2022)
- [ReCoSeg++:Extended Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation](https://mlsanity.com/paper/2508.01058/) (2025)
- [Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions](https://mlsanity.com/paper/2203.10507/) (2022)
- [Performance of GAN-based augmentation for deep learning COVID-19 image classification](https://mlsanity.com/paper/2304.09067/) (2023)
- [A Deep Generative Model of Neonatal Cortical Surface Development](https://mlsanity.com/paper/2206.07542/) (2022)
- [Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency](https://mlsanity.com/paper/2305.16922/) (2023)
- [Causal Image Synthesis of Brain MR in 3D](https://mlsanity.com/paper/2303.14349/) (2023)
- [Learned Local Attention Maps for Synthesising Vessel Segmentations](https://mlsanity.com/paper/2308.12861/) (2023)
- [DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans](https://mlsanity.com/paper/2310.15328/) (2023)
- [3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images](https://mlsanity.com/paper/2311.04049/) (2023)
- [Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis](https://mlsanity.com/paper/2407.05761/) (2024)
- [Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field](https://mlsanity.com/paper/2411.14418/) (2024)
- [Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest](https://mlsanity.com/paper/2501.15572/) (2025)
- [Memory-Efficient 3D High-Resolution Medical Image Synthesis Using CRF-Guided GANs](https://mlsanity.com/paper/2503.10899/) (2025)
- [Characterizing Nash Equilibria in Zero-Sum Games: A Physics-Inspired, Parallelizable Approach with a Linear Number of Gradient Queries](https://mlsanity.com/paper/2507.11366/) (2025)
- [SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions](https://mlsanity.com/paper/2510.26961/) (2025)
- [Precise Liver Tumor Segmentation in CT Using a Hybrid Deep Learning-Radiomics Framework](https://mlsanity.com/paper/2512.07574/) (2025)
- [A Novel Global Context-aware Deep Neural Network for Enhanced Brain Tumor Segmentation using Magnetic Resonance Images](https://mlsanity.com/paper/2605.30510/) (2026)
- [TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model](https://mlsanity.com/paper/2607.13812/) (2026)

## Abstract (arXiv)

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i.e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core. Although brain tumours can easily be detected using multi-modal MRI, accurate tumor segmentation is a challenging task. Hence, using the data provided by the BraTS Challenge 2020, we propose a 3D volume-to-volume Generative Adversarial Network for segmentation of brain tumours. The model, called Vox2Vox, generates realistic segmentation outputs from multi-channel 3D MR images, segmenting the whole, core and enhancing tumor with mean values of 87.20%, 81.14%, and 78.67% as dice scores and 6.44mm, 24.36mm, and 18.95mm for Hausdorff distance 95 percentile for the BraTS testing set after ensembling 10 Vox2Vox models obtained with a 10-fold cross-validation.