# E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge

arXiv: https://arxiv.org/abs/2110.02519 · HTML: https://mlsanity.com/paper/2110.02519/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2021.

## Built on by (most cited first, 8 of 8)

- [ReCo-KD: Region- and Context-Aware Knowledge Distillation for Efficient 3D Medical Image Segmentation](https://mlsanity.com/paper/2601.08301/) (2026)
- [PriorNet: lesion segmentation in PET-CT including prior tumor appearance information](https://mlsanity.com/paper/2210.02203/) (2022)
- [Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net](https://mlsanity.com/paper/2305.06203/) (2023)
- [Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/2503.20446/) (2025)
- [Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)](https://mlsanity.com/paper/2504.13200/) (2025)
- [Ensemble Learning with Residual Transformer for Brain Tumor Segmentation](https://mlsanity.com/paper/2308.00128/) (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)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in medical image segmentation tasks. A common feature in most top-performing CNNs is an encoder-decoder architecture inspired by the U-Net. For multi-region brain tumor segmentation, 3D U-Net architecture and its variants provide the most competitive segmentation performances. In this work, we propose an interesting extension of the standard 3D U-Net architecture, specialized for brain tumor segmentation. The proposed network, called E1D3 U-Net, is a one-encoder, three-decoder fully-convolutional neural network architecture where each decoder segments one of the hierarchical regions of interest: whole tumor, tumor core, and enhancing core. On the BraTS 2018 validation (unseen) dataset, E1D3 U-Net demonstrates single-prediction performance comparable with most state-of-the-art networks in brain tumor segmentation, with reasonable computational requirements and without ensembling. As a submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge, we also evaluate our proposal on the BraTS 2021 dataset. E1D3 U-Net showcases the flexibility in the standard 3D U-Net architecture which we exploit for the task of brain tumor segmentation.