# Context Aware 3D UNet for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2010.13082 · HTML: https://mlsanity.com/paper/2010.13082/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2020.

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

- [Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis](https://mlsanity.com/paper/2210.13336/) (2022)
- [Self-supervised Tumor Segmentation through Layer Decomposition](https://mlsanity.com/paper/2109.03230/) (2021)
- [Unsupervised Federated Domain Adaptation for Segmentation of MRI Images](https://mlsanity.com/paper/2401.02941/) (2024)
- [Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation](https://mlsanity.com/paper/2405.13304/) (2024)

## Abstract (arXiv)

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contextual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contextual information from the concept of feature reusability. In addition, residual-inception blocks (RIB) are used to extract the local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multi-modal brain tumor segmentation challenge (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively.