BraTS2020 Dataset (Training + Validation)

### Context

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 2020 utilizes multi-institutional pre-operative MRI scans and primarily focuses on the segmentation (Task 1) of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’20 also focuses on the prediction of patient overall survival (Task 2), and the distinction between pseudoprogression and true tumor recurrence (Task 3), via integrative analyses of radiomic features and machine learning algorithms. Finally, BraTS'20 intends to evaluate the algorithmic uncertainty in tumor segmentation (Task 4).

### Tasks' Description and Evaluation Framework

In this year's challenge, 4 reference standards are used for the 4 tasks of the challenge:
1. `Manual segmentation labels of tumor sub-regions`,
2. `Clinical data of overall survival`,
3. `Clinical evaluation of progression status`,
4. `Uncertainty estimation for the predicted tumor sub-regions`.

### Imaging Data Description

All BraTS multimodal scans are available as NIfTI files (.nii.gz) and describe a) native (T1) and b) post-contrast T1-weighted (T1Gd), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, and were acquired with different clinical protocols and various scanners from multiple (n=19) institutions, mentioned as data contributors here.

All the imaging datasets have been segmented manually, by one to four raters, following the same annotation protocol, and their annotations were approved by experienced neuro-radiologists. Annotations comprise the GD-enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2), and the necrotic and non-enhancing tumor core (NCR/NET — label 1), as described both in the BraTS 2012-2013 TMI paper and in the latest BraTS summarizing paper. The provided data are distributed after their pre-processing, i.e., co-registered to the same anatomical template, interpolated to the same resolution (1 mm^3) and skull-stripped.

### Use of Data Beyond BraTS

Participants are allowed to use additional public and/or private data (from their own institutions) for data augmentation, only if they also report results using only the BraTS'20 data and discuss any potential difference in their papers and results. This is due to our intentions to provide a fair comparison among the participating methods.

###Data Usage Agreement / Citations:

****You are free to use and/or refer to the BraTS datasets in your own research, provided that you always cite the following three manuscripts:****

[1] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) [ DOI: 10.1109/TMI.2014.2377694 ](https://ieeexplore.ieee.org/document/6975210)

[2] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) [DOI: 10.1038/sdata.2017.117](https://www.nature.com/articles/sdata2017117)

[3] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint [arXiv:1811.02629 (2018)](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjqhYaEvZLqAhUCzjgGHdNODgIQFjAAegQIAxAB&url=https%3A%2F%2Farxiv.org%2Fabs%2F1811.02629&usg=AOvVaw1SAs9r4GA8aHMWLuJffKY6)

****In addition, if there are no restrictions imposed from the journal/conference you submit your paper about citing "Data Citations", please be specific and also cite the following:****

[4] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.KLXWJJ1Q

[5] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.GJQ7R0EF

### 上下文
BraTS项目始终致力于对多模态磁共振成像（MRI）扫描中脑肿瘤的最新分割方法进行评估。BraTS 2020项目采用多机构的术前MRI扫描数据，主要关注具有内在异质性（外观、形状和病理学特征）的脑肿瘤，即胶质瘤的分割（任务1）。此外，为了明确分割任务的临床意义，BraTS’20还聚焦于通过整合放射组学特征和机器学习算法，对患者总生存期的预测（任务2）以及假性进展与真实肿瘤复发的鉴别（任务3）。最终，BraTS'20旨在评估肿瘤分割中的算法不确定性（任务4）。

### 任务描述与评估框架
在本年度的挑战中，针对四个任务使用了4个参考标准：
1. 肿瘤亚区域的手动分割标签，
2. 患者总生存期的临床数据，
3. 进展状态的临床评估，
4. 预测肿瘤亚区域的概率估计。

### 成像数据描述
所有BraTS多模态扫描均以NIfTI文件（.nii.gz）的形式提供，描述了以下内容：a) 原始（T1）和b) 增强T1加权（T1Gd），c) T2加权（T2），以及d) T2流空反转恢复（T2-FLAIR）体积，这些数据通过不同的临床协议和多种扫描设备从多个（n=19）机构采集，此处提及的数据贡献者。
所有成像数据集均由一名至四名评审员按照相同的标注协议进行手动分割，并由经验丰富的神经放射科医生进行审核。标注包括GD增强肿瘤（ET — 标签4）、肿瘤周围水肿（ED — 标签2）以及坏死和非增强肿瘤核心（NCR/NET — 标签1），如BraTS 2012-2013 TMI论文及最新的BraTS总结论文中所述。提供的数据在预处理后进行分发，即与相同的解剖模板配准，插值到相同的分辨率（1 mm^3）并进行去颅骨处理。

### BraTS数据的使用
参赛者可以使用额外的公共和/或私有数据（来自他们自己的机构）进行数据增强，前提是他们同时报告仅使用BraTS'20数据的结果，并在论文和结果中讨论任何潜在的差异。这是由于我们旨在为参与的方法提供公平的比较。

### 数据使用协议/引用
您有权在您自己的研究中使用和/或引用BraTS数据集，前提是您始终引用以下三篇论文：
[1] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) [ DOI: 10.1109/TMI.2014.2377694 ](https://ieeexplore.ieee.org/document/6975210)
[2] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) [DOI: 10.1038/sdata.2017.117](https://www.nature.com/articles/sdata2017117)
[3] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint [arXiv:1811.02629 (2018)](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjqhYaEvZLqAhUCzjgGHdNODgIQFjAAegQIAxAB&url=https%3A%2F%2Farxiv.org%2Fabs%2F1811.02629&usg=AOvVaw1SAs9r4GA8aHMWLuJffKY6)

此外，如果您的论文提交的期刊/会议没有对数据引用施加限制，请具体引用以下内容：
[4] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.KLXWJJ1Q
[5] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.GJQ7R0EF