# Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation

arXiv: https://arxiv.org/abs/2307.16143 · HTML: https://mlsanity.com/paper/2307.16143/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 30 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network](https://mlsanity.com/paper/1802.09655/) (2018)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks](https://mlsanity.com/paper/1911.11897/) (2019)
- [Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN](https://mlsanity.com/paper/1809.04536/) (2018)
- [Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration](https://mlsanity.com/paper/2104.07056/) (2021)
- [SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation](https://mlsanity.com/paper/2105.07044/) (2021)
- [Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks](https://mlsanity.com/paper/2106.08557/) (2021)

## Built on by (most cited first, 19 of 19)

- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model](https://mlsanity.com/paper/2407.05059/) (2024)
- [Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis](https://mlsanity.com/paper/2406.18967/) (2024)
- [Rethinking Perceptual Metrics for Medical Image Translation](https://mlsanity.com/paper/2404.07318/) (2024)
- [MRGen: Segmentation Data Engine for Underrepresented MRI Modalities](https://mlsanity.com/paper/2412.04106/) (2024)
- [ContourDiff: Unpaired Medical Image Translation with Structural Consistency](https://mlsanity.com/paper/2403.10786/) (2024)
- [Boosting Medical Image Synthesis via Registration-guided Consistency and Disentanglement Learning](https://mlsanity.com/paper/2407.07660/) (2024)
- [HC3L-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesis](https://mlsanity.com/paper/2411.01575/) (2024)
- [Schr"odinger Diffusion Driven Signal Recovery in 3T BOLD fMRI Using Unmatched 7T Observations](https://mlsanity.com/paper/2504.01004/) (2025)
- [Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation](https://mlsanity.com/paper/2510.19679/) (2025)
- [Longitudinal Causal Image Synthesis](https://mlsanity.com/paper/2410.17691/) (2024)
- [Fully Guided Neural Schr"odinger bridge for Brain MR image synthesis](https://mlsanity.com/paper/2501.14171/) (2025)
- [Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance](https://mlsanity.com/paper/2504.09441/) (2025)
- [MM2CT: MR-to-CT translation for multi-modal image fusion with mamba](https://mlsanity.com/paper/2508.05476/) (2025)
- [Flip Distribution Alignment VAE for Multi-Phase MRI Synthesis](https://mlsanity.com/paper/2510.02970/) (2025)
- [ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion](https://mlsanity.com/paper/2511.17068/) (2025)
- [Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning](https://mlsanity.com/paper/2603.23295/) (2026)
- [LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators](https://mlsanity.com/paper/2605.19060/) (2026)
- [Prob-BBDM: a Probabilistic Brownian Bridge Diffusion Model for MRI sequence image-to-image translation](https://mlsanity.com/paper/2606.24313/) (2026)

## Abstract (arXiv)

Medical image synthesis is a challenging task due to the scarcity of paired data. Several methods have applied CycleGAN to leverage unpaired data, but they often generate inaccurate mappings that shift the anatomy. This problem is further exacerbated when the images from the source and target modalities are heavily misaligned. Recently, current methods have aimed to address this issue by incorporating a supplementary segmentation network. Unfortunately, this strategy requires costly and time-consuming pixel-level annotations. To overcome this problem, this paper proposes MaskGAN, a novel and cost-effective framework that enforces structural consistency by utilizing automatically extracted coarse masks. Our approach employs a mask generator to outline anatomical structures and a content generator to synthesize CT contents that align with these structures. Extensive experiments demonstrate that MaskGAN outperforms state-of-the-art synthesis methods on a challenging pediatric dataset, where MR and CT scans are heavily misaligned due to rapid growth in children. Specifically, MaskGAN excels in preserving anatomical structures without the need for expert annotations. The code for this paper can be found at https://github.com/HieuPhan33/MaskGAN.