首页> 外文期刊>Archives of Computational Methods in Engineering >A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction
【24h】

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

机译:自动胶质瘤脑肿瘤细分和整体患者存活预测的调查与分析

获取原文
获取原文并翻译 | 示例

摘要

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.
机译:胶质瘤是最致命的脑肿瘤,具有高死亡率。人类专家的治疗计划取决于物理症状的适当诊断以及磁共振(MR)图像分析。在大小,形状,位置和大体积的MR图像方面,脑肿瘤的高度可变性使得分析耗时。自动分割方法可随时降低,具有优异的可再现结果。本文旨在调查胶质瘤脑肿瘤细分的自动化方法的进步。对于基于基准的基准,对各种模型进行客观评估也是必要的。因此,2012-2019 BRATS挑战评估最先进的方法。面临这一挑战的任务的复杂性已经从分段(任务1)生长到整体生存预测(任务2)到对分类的不确定性预测(任务3)。本文涵盖了使用手工特征对任务的深神经网络模型的完整脑肿瘤细分曲线。目的是展示自动脑肿瘤模型中的完全变化。本文还涵盖了涉及脑肿瘤分割和整体生存预测的终端联合模型。探测所有方法,以及影响性能的参数并分析。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号