# Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images

arXiv: https://arxiv.org/abs/2211.01885 · HTML: https://mlsanity.com/paper/2211.01885/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 21 of 21)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Recent progress in semantic image segmentation](https://mlsanity.com/paper/1809.10198/) (2018)
- [CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation](https://mlsanity.com/paper/1904.07979/) (2019)
- [Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras](https://mlsanity.com/paper/1606.03669/) (2016)
- [Systematic study of color spaces and components for the segmentation of sky/cloud images](https://mlsanity.com/paper/1701.04520/) (2017)
- [Multi-label Cloud Segmentation Using a Deep Network](https://mlsanity.com/paper/1903.06562/) (2019)
- [A predictive analytics approach for stroke prediction using machine learning and neural networks](https://mlsanity.com/paper/2203.00497/) (2022)
- [DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs](https://mlsanity.com/paper/2204.06454/) (2022)
- [High-Dynamic-Range Imaging for Cloud Segmentation](https://mlsanity.com/paper/1803.01071/) (2018)
- [Using GANs to Augment Data for Cloud Image Segmentation Task](https://mlsanity.com/paper/2106.03064/) (2021)
- [Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices](https://mlsanity.com/paper/2206.02358/) (2022)
- [Validating Clustering Frameworks for Electric Load Demand Profiles](https://mlsanity.com/paper/2103.00030/) (2021)
- [Identifying Stroke Indicators Using Rough Sets](https://mlsanity.com/paper/2110.10152/) (2021)
- [A Clustering Framework for Residential Electric Demand Profiles](https://mlsanity.com/paper/2105.08537/) (2021)
- [Analyzing the impact of feature selection on the accuracy of heart disease prediction](https://mlsanity.com/paper/2206.03239/) (2022)
- [Localizing Adverts in Outdoor Scenes](https://mlsanity.com/paper/1905.02106/) (2019)
- [Identifying Candidate Spaces for Advert Implantation](https://mlsanity.com/paper/1910.03227/) (2019)

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

- [Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentation](https://mlsanity.com/paper/2507.04862/) (2025)
- [CNN Explainability with Multivector Tucker Saliency Maps for Self-Supervised Models](https://mlsanity.com/paper/2410.23072/) (2024)
- [SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions](https://mlsanity.com/paper/2510.26961/) (2025)
- [VGG16-MCA UNet: Whole-Tumor Segmentation in 2D FLAIR MRI with Decoder-Side Channel Attention](https://mlsanity.com/paper/2609.23919/) (2026)

## Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net. Apart from providing real-time segmentation of MRI scans, the proposed architecture does not need large amount of data to train the proposed lightweight U-Net. Moreover, no additional data augmentation step is required. The lightweight U-Net shows very promising results on BITE dataset and it achieves a mean intersection-over-union (IoU) of 89% while outperforming the standard benchmark algorithms. Additionally, this work demonstrates an effective use of the three perspective planes, instead of the original three-dimensional volumetric images, for simplified brain tumor segmentation.