This dataset includes brain MRI scans of adult brain glioma patients, comprising of 4 structural modalities (i.e., T1, T1c, T2, T2-FLAIR) and associated manually generated ground truth labels for each tumor sub-region (enhancement, necrosis, edema), as well as their MGMT promoter methylation status. These scans are a collection of data from existing TCIA collections, but also cases provided by individual institutions and willing to share with a cc-by license. The BraTS dataset describes a retrospective collection of brain tumor structural mpMRI scans of 2,040 patients (1,480 here), acquired from multiple different institutions under standard clinical conditions, but with different equipment and imaging protocols, resulting in a vastly heterogeneous image quality reflecting diverse clinical practice across different institutions. The 4 structural mpMRI scans included in the BraTS challenge describe a) native (T1) and b) post-contrast T1-weighted (T1Gd (Gadolinium)), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, acquired with different protocols and various scanners from multiple institutions. Furthermore, data on the O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is provided as a binary label. Notably, MGMT is a DNA repair enzyme that the methylation of its promoter in newly diagnosed glioblastoma has been identified as a favorable prognostic factor and a predictor of chemotherapy response. It is curated for computational image analysis of segmentation and prediction of the MGMT promoter methylation status.

A note about available TCIA data which were converted for use in this Challenge: (Training, Validation, Test)

The RSNA-ASNR-MICCAI BraTS 2021 challenge utilizes multi-institutional pre-operative baseline multi-parametric magnetic resonance imaging (mpMRI) scans, and focuses on the evaluation of state-of-the-art methods for (Task 1) the segmentation of intrinsically heterogeneous brain glioblastoma sub-regions in mpMRI scans. Furthemore, this BraTS 2021 challenge also focuses on the evaluation of (Task 2) classification methods to predict the MGMT promoter methylation status.

Multi-parametric MRI scans from 2000 patients were used for BraTS2021, 1251 of which were provided with segmentation labels to the participants for developing their algorithms, 219 of which were used for the public leaderboard during the validation phase, and the remaining 530 cases were intended for the private leaderboard and the final ranking of the participants.

Comparison of whole tumor segmentation with BRATS 2021.

The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan. The dataset also provides full masks for brain tumors, with labels for ED, ET, NET/NCR. The segmentation evaluation is based on three tasks: WT, TC and ET segmentation.

Performance comparisons of subclass tumor using Brats 2021.

This dataset is a collection of synthetic images generated by 5 generative models (Progressive GAN, StyleGAN1, StyleGAN2, StyleGAN3, diffusion model) trained on the BraTS 2020 and 2021 datasets 1,2,3,4,5. The trained generative models are also shared in this dataset. See our recent work [6] for more information, and a comparison of training segmentation networks with real and synthetic images.

This brain tumor dataset contains 3064 T1-weighted contrast-inhanced images with three kinds of brain tumor. Detailed information of the dataset can be found in the readme file.The README file is updated:Add image acquisition protocolAdd MATLAB code to convert .mat file to jpg images

RSNA MICCAI NPY

Transformed PNG images to NPY format

This dataset contains the "DICOM data" of the training dataset of the RSNA-MICCAI Brain Tumor Radiogenomic Classification challenge in NPY format. It is a bit bigger than the original.

Notes

• Images sizes have been resized to 256 x 256 x 256 to have a uniform dataset.
• The images that weren't in the dataset are simply substituted by a zero-valued 3D image.
• The structure of the dataset has changed to have four subfolders with all the data instead of 500+ subfolders. Changing from index to view.

References:

U.Baid, et al., “The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification”, arXiv:2107.02314, 2021.

RSNA Processed Voxels 64x256x256 CLAHE

License: As per the competition: https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/data On using this dataset, you agree to the RSNA-MICCAI Brain Tumor Radiogenomic Classification competition rules.

Overview

This dataset contains the processed & resampled RSNA-MICCAI Brain Tumor Radiogenomic Classification data, used to predict the status of a genetic biomarker important for brain cancer treatment, in the form of '.npy' 3D voxels.

The following processing were done:

 ✔️ Align & Crop  ✔️ Filter Out Slices with Less Information  ✔️ CLAHE Contrast Enhancement & Normalization Across the 3D Volume  ✔️ Resampled to size 64 x 256 x 256 for Optimal Training

📌 Usage Notebook:   [TPU] RSNA Keras 3D CNN Voxel Train 🏷️🏂

📌 Dataset Creation Notebook:   RSNA 3D CLAHE Voxels + TPU 3D Augmentations🌃🚅

Acknowledgements

U.Baid, et al., “The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification”, arXiv:2107.02314, 2021.

International challenges have become the standard for validation of biomedical image analysis methods. We argue, though, that the actual performance even of the winning algorithms on ���real-world��� clinical data often remains unclear, as the data included in these challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and ownership hurdles. As the first challenge to ever be proposed for federated learning in medicine, the Federated Tumor Segmentation (FeTS) challenge 2021 intends to address these hurdles, both for the creation and the evaluation of tumor segmentation models. Specifically, the FeTS 2021 challenge uses clinically acquired, multi-institutional MRI scans from the BraTS challenge, as well as from various remote independent institutions included in the collaborative network of a real-world federation (www.fets.ai). The FeTS challenge focuses on the construction and evaluation of a consensus model for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas [1]. Compared to the BraTS challenge [2-4], the ultimate goal of FeTS is 1) the creation of a consensus segmentation model that has gained knowledge from data of multiple institutions without pooling their data together (i.e., by retaining the data within each institution), and 2) the evaluation of segmentation models in such a federated configuration (i.e., in the wild). The FeTS 2021 challenge is structured in two tasks: Task 1 ("Federated Training") aims at effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages. Task 2 ("Federated Evaluation") aims at robust segmentation algorithms, given a pre-defined weight aggregation method, evaluated during the testing phase on unseen datasets from various remote independent institutions of the collaborative network of the fets.ai federation. To prepare for both these tasks, the participants can use the information provided on data origin and acquisition settings during the training phase of the challenge. We intend to add a third task in the FeTS challenge 2022 to account for adversaries during the training phase. The clinical relevance and importance of the FeTS challenge is that it addresses challenges related to privacy, legal, bureaucratic, and ownership concerns. Ground truth reference annotations are created and approved by expert neuroradiologists for every subject included in the training, validation, and testing datasets to quantitatively evaluate the performance of the participating algorithms. Participants are free to choose whether they want to focus on only one or multiple tasks. References [1] M.J.Sheller, et al. "Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data." Scientific reports. 10:1-12, 2020. DOI: 10.1038/s41598-020-69250-1 [2] B. H. Menze, 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 [3] S.Bakas, 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 [4] S. Bakas, 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 [5] T. Rohlfing, et al. The SRI24 multichannel atlas of normal adult human brain structure. Hum Brain Mapp. 31(5):798-819, 2010. [6] Duan R, et al. PALM: Patient-centered Treatment Ranking via Large-scale Multivariate Network Meta-analysis. medRxiv. 2020. [7] A. L. Simpson et al., ���A large annotated medical image dataset for the development and evaluation of segmentation algorithms,��� arXiv:1902.09063 [8] M. Wiesenfarth, et al. ���Methods and open-source toolkit for analyzing and visualizing challenge results,��� arXiv:1910.05121. [9] L. Maier-Hein, et al., ���Why rankings of biomedical image analysis competitions should be interpreted with care,��� Nat. Commun., 9(1):1���13, 2018. DOI: 10.1038/s41467-018-07619-7 [10] R.Cox, et al. ���A (Sort of) new image data format standard: NIfTI-1: WE 150���, Neuroimage, 22, 2004. [11] S.Thakur, et al. ���Brain Extraction on MRI Scans in Presence of Diffuse Glioma: Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training���, NeuroImage, 220: 117081, 2020. DOI: 10.1016/j.neuroimage.2020.117081

PurposeMachine learning has been applied to the diagnostic imaging of gliomas to augment classification, prognostication, segmentation, and treatment planning. A systematic literature review was performed to identify how machine learning has been applied to identify gliomas in datasets which include non-glioma images thereby simulating normal clinical practice.Materials and MethodsFour databases were searched by a medical librarian and confirmed by a second librarian for all articles published prior to February 1, 2021: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science-Core Collection. The search strategy included both keywords and controlled vocabulary combining the terms for: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, as well as related terms. The review was conducted in stepwise fashion with abstract screening, full text screening, and data extraction. Quality of reporting was assessed using TRIPOD criteria.ResultsA total of 11,727 candidate articles were identified, of which 12 articles were included in the final analysis. Studies investigated the differentiation of normal from abnormal images in datasets which include gliomas (7 articles) and the differentiation of glioma images from non-glioma or normal images (5 articles). Single institution datasets were most common (5 articles) followed by BRATS (3 articles). The median sample size was 280 patients. Algorithm testing strategies consisted of five-fold cross validation (5 articles), and the use of exclusive sets of images within the same dataset for training and for testing (7 articles). Neural networks were the most common type of algorithm (10 articles). The accuracy of algorithms ranged from 0.75 to 1.00 (median 0.96, 10 articles). Quality of reporting assessment utilizing TRIPOD criteria yielded a mean individual TRIPOD ratio of 0.50 (standard deviation 0.14, range 0.37 to 0.85).ConclusionSystematic review investigating the identification of gliomas in datasets which include non-glioma images demonstrated multiple limitations hindering the application of these algorithms to clinical practice. These included limited datasets, a lack of generalizable algorithm training and testing strategies, and poor quality of reporting. The development of more robust and heterogeneous datasets is needed for algorithm development. Future studies would benefit from using external datasets for algorithm testing as well as placing increased attention on quality of reporting standards.Systematic Review Registrationwww.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020209938, International Prospective Register of Systematic Reviews (PROSPERO 2020 CRD42020209938).