13 datasets found

The BraTS 2019 validation and testing datasets are used to evaluate the performance of the proposed segmentation method.

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...

A large annotated medical image dataset for the development and evaluation of segmentation algorithms.

Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis,...

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,...

One-pass multi-task convolutional neural networks for efficient brain tumor segmentation.

No NewNet: A simple and efficient approach to brain tumor segmentation.

3D MRI brain tumor segmentation using deep convolutional neural networks.

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BraTS challenge.

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

The multimodal brain tumor image segmentation benchmark (brats).

The multi-center BraTS 2019 dataset is used to perform cross-modality image-to-image synthesis and investigate domain adaptation.

The BraTS21 dataset consists of 1251 brain MRI scans of four different weightings (T1, T1-CE, T2, FLAIR).