Glioma is the deadliest 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 make 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 evaluate the state-of-the-art methods. The complexity of the tasks facing this challenge has grown from segmentation (Task 1) 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.

胶质瘤是死亡率最高的最致命的脑肿瘤。人类专家的治疗计划取决于对身体症状的正确诊断以及磁共振（MR）图像分析。脑肿瘤在大小，形状，位置和大量MR图像方面的高度可变性使分析非常耗时。自动分割方法可减少时间，并具有出色的可重复结果。本文旨在调查胶质瘤脑肿瘤分割自动化方法的进展。基于基准对各种模型进行客观评估也很重要。因此，2012-2019年BraTS挑战评估了最先进的方法。面临这一挑战的任务的复杂性已经从细分（任务1）到总体生存预测（任务2），再到不确定性分类预测（任务3）。本文涵盖了使用手工特征对任务1的深层神经网络模型进行脑肿瘤分割的全部范围。目的是展示自动化脑肿瘤模型趋势的完整变化。本文还涵盖了涉及脑肿瘤分割和整体生存预测的端到端关节模型。对所有方法进行了探查，并对影响性能的参数进行了制表和分析。本文还涵盖了涉及脑肿瘤分割和整体生存预测的端到端关节模型。对所有方法进行了探查，并对影响性能的参数进行了制表和分析。本文还涵盖了涉及脑肿瘤分割和整体生存预测的端到端关节模型。对所有方法进行了探查，并对影响性能的参数进行了制表和分析。