# Segmentation of 2D Brain MR Images

arXiv: https://arxiv.org/abs/2111.03370 · HTML: https://mlsanity.com/paper/2111.03370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning](https://mlsanity.com/paper/1810.12241/) (2018)
- [Unsupervised Deep Learning for Bayesian Brain MRI Segmentation](https://mlsanity.com/paper/1904.11319/) (2019)
- [Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images](https://mlsanity.com/paper/1809.07786/) (2018)
- [Enforcing temporal consistency in Deep Learning segmentation of brain MR images](https://mlsanity.com/paper/1906.07160/) (2019)

## Abstract (arXiv)

Brain tumour segmentation is an essential task in medical image processing. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumours for cancer diagnosis, from large number of MRI images, is both a difficult and time-consuming task. There is a need for automatic brain tumour image segmentation. The purpose of this project is to provide an automatic brain tumour segmentation method of MRI images to help locate the tumour accurately and quickly.