# Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2108.06772 · HTML: https://mlsanity.com/paper/2108.06772/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 15 Aug 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [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)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction](https://mlsanity.com/paper/1807.07716/) (2018)
- [Joint segmentation and classification of retinal arteries/veins from fundus images](https://mlsanity.com/paper/1903.01330/) (2019)
- [Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation](https://mlsanity.com/paper/1902.01040/) (2019)
- [Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module](https://mlsanity.com/paper/1707.07128/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation](https://mlsanity.com/paper/2408.01191/) (2024)

## Abstract (arXiv)

Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance. Recently, various models based on deep neural networks have been proposed for the pixel-level segmentation of tumors in brain MRIs. However, the structural variations, spatial dissimilarities, and intensity inhomogeneity in MRIs make segmentation a challenging task. We propose a new end-to-end brain tumor segmentation architecture based on U-Net that integrates Inception modules and dilated convolutions into its contracting and expanding paths. This allows us to extract local structural as well as global contextual information. We performed segmentation of glioma sub-regions, including tumor core, enhancing tumor, and whole tumor using Brain Tumor Segmentation (BraTS) 2018 dataset. Our proposed model performed significantly better than the state-of-the-art U-Net-based model (p<0.05) for tumor core and whole tumor segmentation.