# Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

arXiv: https://arxiv.org/abs/2201.03777 · HTML: https://mlsanity.com/paper/2201.03777/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/1810.00236/) (2018)
- [Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation](https://mlsanity.com/paper/1806.07201/) (2018)
- [Vox2Vox: 3D-GAN for Brain Tumour Segmentation](https://mlsanity.com/paper/2003.13653/) (2020)

## Built on by (most cited first, 9 of 9)

- [LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation](https://mlsanity.com/paper/2404.05911/) (2024)
- [Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation](https://mlsanity.com/paper/2209.07704/) (2022)
- [Tumor-Centered Patching for Enhanced Medical Image Segmentation](https://mlsanity.com/paper/2308.12168/) (2023)
- [Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs](https://mlsanity.com/paper/2403.05340/) (2024)
- [Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection](https://mlsanity.com/paper/2408.14847/) (2024)
- [Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views](https://mlsanity.com/paper/2410.17502/) (2024)
- [UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion](https://mlsanity.com/paper/2505.03494/) (2025)
- [Unified Multimodal Coherent Field: Synchronous Semantic-Spatial-Vision Fusion for Brain Tumor Segmentation](https://mlsanity.com/paper/2509.17520/) (2025)
- [Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis](https://mlsanity.com/paper/2602.15067/) (2026)

## Abstract (arXiv)

This paper proposes an adversarial learning based training approach for brain tumor segmentation task. In this concept, the 3D segmentation network learns from dual reciprocal adversarial learning approaches. To enhance the generalization across the segmentation predictions and to make the segmentation network robust, we adhere to the Virtual Adversarial Training approach by generating more adversarial examples via adding some noise on original patient data. By incorporating a critic that acts as a quantitative subjective referee, the segmentation network learns from the uncertainty information associated with segmentation results. We trained and evaluated network architecture on the RSNA-ASNR-MICCAI BraTS 2021 dataset. Our performance on the online validation dataset is as follows: Dice Similarity Score of 81.38%, 90.77% and 85.39%; Hausdorff Distance (95%) of 21.83 mm, 5.37 mm, 8.56 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our approach achieved a Dice Similarity Score of 84.55%, 90.46% and 85.30%, as well as Hausdorff Distance (95%) of 13.48 mm, 6.32 mm and 16.98 mm on the final test dataset. Overall, our proposed approach yielded better performance in segmentation accuracy for each tumor sub-region. Our code implementation is publicly available at https://github.com/himashi92/vizvivabrats2021