# Optimized U-Net for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2110.03352 · HTML: https://mlsanity.com/paper/2110.03352/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [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)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [DropBlock: A regularization method for convolutional networks](https://mlsanity.com/paper/1810.12890/) (2018)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Deeply-Supervised CNN for Prostate Segmentation](https://mlsanity.com/paper/1703.07523/) (2017)
- [Cascaded V-Net using ROI masks for brain tumor segmentation](https://mlsanity.com/paper/1812.11588/) (2018)

## Built on by (most cited first, 30 of 40)

- [CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.07370/) (2022)
- [FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy](https://mlsanity.com/paper/2207.01390/) (2022)
- [Topology-Aware Focal Loss for 3D Image Segmentation](https://mlsanity.com/paper/2304.12223/) (2023)
- [MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder](https://mlsanity.com/paper/2310.19898/) (2023)
- [High-Resolution Swin Transformer for Automatic Medical Image Segmentation](https://mlsanity.com/paper/2207.11553/) (2022)
- [Continuous U-Net: Faster, Greater and Noiseless](https://mlsanity.com/paper/2302.00626/) (2023)
- [AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor](https://mlsanity.com/paper/2306.14505/) (2023)
- [Large-Kernel Attention for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.11225/) (2022)
- [Fetal-BET: Brain Extraction Tool for Fetal MRI](https://mlsanity.com/paper/2310.01523/) (2023)
- [Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation](https://mlsanity.com/paper/2308.07251/) (2023)
- [Federated brain tumor segmentation: an extensive benchmark](https://mlsanity.com/paper/2410.17265/) (2024)
- [An Ensemble Approach for Brain Tumor Segmentation and Synthesis](https://mlsanity.com/paper/2411.17617/) (2024)
- [Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT](https://mlsanity.com/paper/2208.06313/) (2022)
- [An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation](https://mlsanity.com/paper/2402.07008/) (2024)
- [Robust Conformal Volume Estimation in 3D Medical Images](https://mlsanity.com/paper/2407.19938/) (2024)
- [ReCoSeg++:Extended Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation](https://mlsanity.com/paper/2508.01058/) (2025)
- [Negligible effect of brain MRI data preprocessing for tumor segmentation](https://mlsanity.com/paper/2204.05278/) (2022)
- [AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net](https://mlsanity.com/paper/2309.12114/) (2023)
- [Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction](https://mlsanity.com/paper/2401.00587/) (2023)
- [Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning](https://mlsanity.com/paper/2403.02566/) (2024)
- [Multi-Modality Abdominal Multi-Organ Segmentation with Deep Supervised 3D Segmentation Model](https://mlsanity.com/paper/2208.12041/) (2022)
- [Prediction of Diblock Copolymer Morphology via Machine Learning](https://mlsanity.com/paper/2308.16886/) (2023)
- [Whole-brain radiomics for clustered federated personalization in brain tumor segmentation](https://mlsanity.com/paper/2310.11480/) (2023)
- [Train-Free Segmentation in MRI with Cubical Persistent Homology](https://mlsanity.com/paper/2401.01160/) (2024)
- [Carefully Structured Compression: Efficiently Managing StarCraft II Data](https://mlsanity.com/paper/2410.08659/) (2024)
- [A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2412.06088/) (2024)
- [crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023](https://mlsanity.com/paper/2506.12006/) (2025)
- [Multimodal Fusion at Three Tiers: Physics-Driven Data Generation and Vision-Language Guidance for Brain Tumor Segmentation](https://mlsanity.com/paper/2507.09966/) (2025)
- [Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients](https://mlsanity.com/paper/2203.05563/) (2022)
- [Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation](https://mlsanity.com/paper/2305.07850/) (2023)

## Abstract (arXiv)

We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge. To find the optimal model architecture and the learning schedule, we have run an extensive ablation study to test: deep supervision loss, Focal loss, decoder attention, drop block, and residual connections. Additionally, we have searched for the optimal depth of the U-Net encoder, number of convolutional channels and post-processing strategy. Our method won the validation phase and took third place in the test phase. We have open-sourced the code to reproduce our BraTS21 submission at the NVIDIA Deep Learning Examples GitHub Repository.