论文标题
对脑肿瘤分割和总体生存预测的端到端方法的综述
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction
论文作者
论文摘要
脑肿瘤分割旨在从健康的脑组织中描绘肿瘤组织。肿瘤组织包括坏死，周围水肿和活性肿瘤。相反，健康的脑组织包括白质，灰质和脑脊液。基于MRI的脑肿瘤分割研究正在广受欢迎： 1。它不会照射X射线或计算机断层扫描成像等电离辐射。 2。它产生内部身体结构的详细图片。 MRI扫描是对基于深度学习的方法的输入，这些方法可用于自动脑肿瘤分割。段的特征被馈送到分类器，以预测患者的整体存活。本文的动机是对最新的共同涵盖脑肿瘤分割和总体生存预测进行广泛概述。
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.
