Multimodal Brain Tumor Segmentation Challenge 2019: Tasks

ScopeRelevanceTasks DataEvaluationParticipation SummaryRegistrationPrevious BraTSPeople



• Task 1: Segmentation of gliomas in pre-operative MRI scans.

The participants are called to address this task by using the provided clinically-acquired training data to develop their method and produce segmentation labels of the different glioma sub-regions. The sub-regions considered for evaluation are: 1) the "enhancing tumor" (ET), 2) the "tumor core" (TC), and 3) the "whole tumor" (WT) [see figure below]. The ET is described by areas that show hyper-intensity in T1Gd when compared to T1, but also when compared to “healthy” white matter in T1Gd. The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor. The appearance of the necrotic (NCR) and the non-enhancing (NET) tumor core is typically hypo-intense in T1-Gd when compared to T1. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.

The provided segmentation labels have values of 1 for NCR & NET, 2 for ED, 4 for ET, and 0 for everything else.
The participants are called to upload their segmentation labels as a single multi-label file in nifti (.nii.gz) format, into CBICA's Image Processing Portal for evaluation.


• Task 2: Prediction of patient overall survival (OS) from pre-operative scans.

Once the participants produce their segmentation labels in the pre-operative scans, they will be called to use these labels in combination with the provided multimodal MRI data to extract imaging/radiomic features that they consider appropriate, and analyze them through machine learning algorithms, in an attempt to predict patient OS. The participants do not need to be limited to volumetric parameters, but can also consider intensity, morphologic, histogram-based, and textural features, as well as spatial information, and glioma diffusion properties extracted from glioma growth models.

Note that participants will be evaluated for the predicted survival status of subjects with resection status of GTR (i.e., Gross Total Resection).
The participants are called to upload a .csv file with the subject ids and the predicted survival values (survival in days), into CBICA's Image Processing Portal for evaluation.


• Task 3: Quantification of Uncertainty in Segmentation.

In BraTS 2019 we decided to experimentally include this complementary research task, which is mainly run by Raghav Mehta, Angelos Filos, Tal Arbel, and Yarin Gal.

This new task focuses on exploring uncertainty measures in the context of glioma region segmentation, with the objective of rewarding participating methods with resulting predictions that are: (a) confident when correct and (b) uncertain when incorrect. Participants willing to participate in this new task are asked to upload (in addition to their segmentation results of Task 1) 3 generated uncertainty maps associated with the resulting labels at every voxel.

The uncertainty maps should be associated with 1) "enhancing tumor" (ET), 2) "tumor core" (TC), and 3) "whole tumor" (WT) regions. In this manner, the uncertainties will be associated with the traditional BraTS Dice metrics. The participants should normalize their uncertainty values between 0 - 100 across the entire dataset, such that "0" represents the most certain prediction and "100" represents the most uncertain. Note that, in any one single patient case, the values of the uncertainties do not need to take on the full range from [0 100] (i.e. The algorithm may be confident for predictions at all voxels for a single patient). To keep storage requirements to a minimum, participants are expected to submit uncertainties in ‘uint8’ type.

The participants are called to upload 4 nifti (.nii.gz) volumes (3 uncertainty maps and 1 multi-class segmentation volume from Task 1) onto CBICA's Image Processing Portal format. For example, for each ID in the dataset, participants are expected to upload following 4 volumes:
1. {ID}.nii.gz (multi-class label map)
2. {ID}_unc_whole.nii.gz (Uncertainty map associated with whole tumor)
3. {ID}_unc_core.nii.gz (Uncertainty map associated with tumor core)
4. {ID}_unc_enhance.nii.gz (Uncertainty map associated with enhancing tumor)


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