# Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images

arXiv: https://arxiv.org/abs/2102.07225 · HTML: https://mlsanity.com/paper/2102.07225/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [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)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Image Super-Resolution by Neural Texture Transfer](https://mlsanity.com/paper/1903.00834/) (2019)
- [Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge](https://mlsanity.com/paper/1904.00682/) (2019)
- [Unpaired Multi-modal Segmentation via Knowledge Distillation](https://mlsanity.com/paper/2001.03111/) (2020)
- [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019)
- [Label-Noise Robust Generative Adversarial Networks](https://mlsanity.com/paper/1811.11165/) (2018)
- [Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image](https://mlsanity.com/paper/1709.02073/) (2017)

## Built on by (most cited first, 4 of 4)

- [Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information](https://mlsanity.com/paper/2112.05758/) (2021)
- [Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification](https://mlsanity.com/paper/2307.00479/) (2023)
- [UESA-Net: U-Shaped Embedded Multidirectional Shrinkage Attention Network for Ultrasound Nodule Segmentation](https://mlsanity.com/paper/2509.22763/) (2025)
- [UnCapsTSR: An Unsupervised Transformer-based Image Super-Resolution Approach for Capsule Endoscopy Images](https://mlsanity.com/paper/2609.02476/) (2026)

## Abstract (arXiv)

Inter-scanner and inter-protocol discrepancy in MRI datasets are known to lead to significant quantification variability. Hence image-to-image or scanner-to-scanner translation is a crucial frontier in the area of medical image analysis with a lot of potential applications. Nonetheless, a significant percentage of existing algorithms cannot explicitly exploit and preserve texture details from target scanners and offers individual solutions towards specialized task-specific architectures. In this paper, we design a multi-scale texture transfer to enrich the reconstruction images with more details. Specifically, after calculating textural similarity, the multi-scale texture can adaptively transfer the texture information from target images or reference images to restored images. Different from the pixel-wise matching space as done by previous algorithms, we match texture features in a multi-scale scheme implemented in the neural space. The matching mechanism can exploit multi-scale neural transfer that encourages the model to grasp more semantic-related and lesion-related priors from the target or reference images. We evaluate our multi-scale texture GAN on three different tasks without any task-specific modifications: cross-protocol super-resolution of diffusion MRI, T1-Flair, and Flair-T2 modality translation. Our multi-texture GAN rehabilitates more high-resolution structures (i.e., edges and anatomy), texture (i.e., contrast and pixel intensities), and lesion information (i.e., tumor). The extensively quantitative and qualitative experiments demonstrate that our method achieves superior results in inter-protocol or inter-scanner translation over state-of-the-art methods.