Brats MICCAI Brain tumor dataset

BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’19 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms. Finally, BraTS'19 intends to experimentally evaluate the uncertainty in tumor segmentations.

脑肿瘤分割挑战赛（BraTS）始终致力于评估多模态磁共振成像（magnetic resonance imaging, MRI）扫描中脑肿瘤分割的前沿方法。BraTS 2019采用多机构的术前MRI扫描，核心任务为对外观、形态及组织学层面均存在内在异质性的脑肿瘤，即神经胶质瘤（gliomas），进行分割。此外，为明确该分割任务的临床相关性，BraTS 2019还通过整合放射组学特征（radiomic features）与机器学习算法，实现患者总生存期的预测。最后，BraTS 2019旨在通过实验手段评估肿瘤分割任务中的不确定性。

搜集汇总

背景与挑战

背景概述

Brats MICCAI Brain tumor dataset是一个包含285例脑肿瘤病例的多模态MRI数据集，数据格式为nifti图像，主要用于脑肿瘤分割算法评估和患者生存期预测研究。数据集包含210例高级别胶质瘤和75例其他类型胶质瘤，每个病例包含T1、T2、Flair和T1Ce四种序列的MRI扫描。