# Extending nn-UNet for brain tumor segmentation

arXiv: https://arxiv.org/abs/2112.04653 · HTML: https://mlsanity.com/paper/2112.04653/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 9 Dec 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [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)
- [Axial Attention in Multidimensional Transformers](https://mlsanity.com/paper/1912.12180/) (2019)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation](https://mlsanity.com/paper/2003.07853/) (2020)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation](https://mlsanity.com/paper/1711.01468/) (2017)

## Built on by (most cited first, 30 of 36)

- [One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation](https://mlsanity.com/paper/2204.13738/) (2022)
- [How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation](https://mlsanity.com/paper/2402.17317/) (2024)
- [CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.07370/) (2022)
- [Neuralizer: General Neuroimage Analysis without Re-Training](https://mlsanity.com/paper/2305.02644/) (2023)
- [3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers](https://mlsanity.com/paper/2310.07781/) (2023)
- [Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution](https://mlsanity.com/paper/2212.09310/) (2022)
- [Preoperative brain tumor imaging: models and software for segmentation and standardized reporting](https://mlsanity.com/paper/2204.14199/) (2022)
- [The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset](https://mlsanity.com/paper/2304.07248/) (2023)
- [High-Resolution Swin Transformer for Automatic Medical Image Segmentation](https://mlsanity.com/paper/2207.11553/) (2022)
- [Large-Kernel Attention for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.11225/) (2022)
- [Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT](https://mlsanity.com/paper/2208.06313/) (2022)
- [Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation](https://mlsanity.com/paper/2209.07704/) (2022)
- [Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation](https://mlsanity.com/paper/2411.04632/) (2024)
- [Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks](https://mlsanity.com/paper/2304.08881/) (2023)
- [Interactive Image Selection and Training for Brain Tumor Segmentation Network](https://mlsanity.com/paper/2406.03225/) (2024)
- [Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation](https://mlsanity.com/paper/2406.16848/) (2024)
- [T1-contrast Enhanced MRI Generation from Multi-parametric MRI for Glioma Patients with Latent Tumor Conditioning](https://mlsanity.com/paper/2409.01622/) (2024)
- [Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation](https://mlsanity.com/paper/2309.05919/) (2023)
- [Brain MRI Screening Tool with Federated Learning](https://mlsanity.com/paper/2311.14086/) (2023)
- [Multi-modal MRI Translation via Evidential Regression and Distribution Calibration](https://mlsanity.com/paper/2407.07372/) (2024)
- [Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis](https://mlsanity.com/paper/2410.18698/) (2024)
- [CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation](https://mlsanity.com/paper/2208.05241/) (2022)
- [Train-Free Segmentation in MRI with Cubical Persistent Homology](https://mlsanity.com/paper/2401.01160/) (2024)
- [Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade](https://mlsanity.com/paper/2410.14020/) (2024)
- [Ensemble Learning with Residual Transformer for Brain Tumor Segmentation](https://mlsanity.com/paper/2308.00128/) (2023)
- [Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers](https://mlsanity.com/paper/2312.11467/) (2023)
- [Self-calibrated convolution towards glioma segmentation](https://mlsanity.com/paper/2402.05218/) (2024)
- [Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models](https://mlsanity.com/paper/2402.07354/) (2024)
- [GASA-UNet: Global Axial Self-Attention U-Net for 3D Medical Image Segmentation](https://mlsanity.com/paper/2409.13146/) (2024)
- [Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding](https://mlsanity.com/paper/2506.00434/) (2025)

## Abstract (arXiv)

Brain tumor segmentation is essential for the diagnosis and prognosis of patients with gliomas. The brain tumor segmentation challenge has continued to provide a great source of data to develop automatic algorithms to perform the task. This paper describes our contribution to the 2021 competition. We developed our methods based on nn-UNet, the winning entry of last year competition. We experimented with several modifications, including using a larger network, replacing batch normalization with group normalization, and utilizing axial attention in the decoder. Internal 5-fold cross validation as well as online evaluation from the organizers showed the effectiveness of our approach, with minor improvement in quantitative metrics when compared to the baseline. The proposed models won first place in the final ranking on unseen test data. The codes, pretrained weights, and docker image for the winning submission are publicly available at https://github.com/rixez/Brats21_KAIST_MRI_Lab