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

TL;DR

This paper provides a comprehensive review of end-to-end deep learning methods for brain tumor segmentation and survival prediction using MRI, highlighting recent advances and challenges in the field.

Contribution

It offers an extensive overview of current state-of-the-art techniques for joint brain tumor segmentation and survival prediction, emphasizing deep learning approaches.

Findings

Deep learning methods improve segmentation accuracy.

MRI-based approaches are safer and more detailed.

Current models show promising survival prediction results.

Abstract

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.