# A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction

arXiv: https://arxiv.org/abs/2006.01632 · HTML: https://mlsanity.com/paper/2006.01632/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 3 of 3)

- [Glioblastoma Multiforme Patient Survival Prediction](https://mlsanity.com/paper/2101.10589/) (2021)
- [FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis](https://mlsanity.com/paper/2402.08582/) (2024)
- [Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis](https://mlsanity.com/paper/2602.15067/) (2026)

## Abstract (arXiv)

Brain tumor segmentation intends to delineate tumor tissues from healthy brain tissues. The tumor tissues include necrosis, peritumoral edema, and active tumor. In contrast, healthy brain tissues include white matter, gray matter, and cerebrospinal fluid. The MRI based brain tumor segmentation research is gaining popularity as; 1. It does not irradiate ionized radiation like X-ray or computed tomography imaging. 2. It produces detailed pictures of internal body structures. The MRI scans are input to deep learning-based approaches which are useful for automatic brain tumor segmentation. The features from segments are fed to the classifier which predict the overall survival of the patient. The motive of this paper is to give an extensive overview of state-of-the-art jointly covering brain tumor segmentation and overall survival prediction.