A REVIEW ON END-TO-END METHODS FOR BRAIN TUMOR SEGMENTATION AND OVERALL SURVIVAL PREDICTION

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Volume 3 (1), June 2020, Pages 119-138

Snehal R. Rajput, Mehul S. Raval

Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

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 that are useful for automatic brain tumor segmentation. The features from segments are fed to the classifier, which predicts the overall survival of the patient. This paper aims to give an extensive overview of the state-of-the-art, jointly covering brain tumor segmentation and overall survival prediction.

Keywords:

Brain, image analysis, neural network, segmentation, tumor

DOI: https://doi.org/10.32010/26166127.2020.3.1.119.138

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