  Glioma is the most deadly brain tumor with high mortality. Treatment planning
by human experts depends on the proper diagnosis of physical symptoms along
with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor
in terms of size, shape, location, and a high volume of MR images makes the
analysis time-consuming. Automatic segmentation methods achieve a reduction in
time with excellent reproducible results. The article aims to survey the
advancement of automated methods for Glioma brain tumor segmentation. It is
also essential to make an objective evaluation of various models based on the
benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates
state-of-the-art methods. The complexity of tasks under the challenge has grown
from segmentation (Task1) to overall survival prediction (Task 2) to
uncertainty prediction for classification (Task 3). The paper covers the
complete gamut of brain tumor segmentation using handcrafted features to deep
neural network models for Task 1. The aim is to showcase a complete change of
trends in automated brain tumor models. The paper also covers end to end joint
models involving brain tumor segmentation and overall survival prediction. All
the methods are probed, and parameters that affect performance are tabulated
and analyzed.
