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BraTS 2018 Training Dataset
The BraTS 2018 training dataset included 285 cases (210 HGG and 75 LGG), each with four 3D MRI modalities (T1, T1c, T2 and FLAIR) rigidly aligned, resampled to 1x1x1 mm... -
3D MRI brain tumor segmentation using autoencoder regularization
Automated segmentation of 3D brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. -
BraTS 2019 validation and testing datasets
The BraTS 2019 validation and testing datasets are used to evaluate the performance of the proposed segmentation method. -
BraTS 2019 training dataset
Multimodal brain tumor segmentation challenge (BraTS) aims to evaluate state-of-the-art methods for the segmentation of brain tumors by providing a 3D MRI dataset with ground... -
BraTS 2020
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis,... -
3D MRI brain tumor segmentation using deep convolutional neural networks
3D MRI brain tumor segmentation using deep convolutional neural networks.
