39 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...

Novel Local Radiomic Bayesian Classifiers for Non-invasive Prediction of MGMT Methylation Status in Glioblastoma

The Multimodal Brain Tumor Segmentation Challenge 2020 dataset was used as our primary dataset for brain tumor classification and segmentation.

The Brain Tumor Segmentation (BraTS) Challenge Africa (BraTS-Africa) dataset, which provides a valuable resource for addressing challenges specific to resource-limited settings,...

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

The Brain Tumor Segmentation (BraTS) Challenge 2021 dataset, which provides a valuable resource for addressing challenges specific to resource-limited settings, particularly the...

The SMU-Net dataset is not explicitly mentioned in the paper, but it is used as a benchmark for the proposed method.

The BraTS 2020 validation dataset contains the same type of MR images from 125 patients, without the ground truth annotations.

The BraTS 2020 challenge dataset is a multimodal MRI brain tumor segmentation dataset. It contains 369 subjects with 4 MRI modalities (T2 weighted FLAIR, T1 weighted, T1...

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

Extending nn-unet for brain tumor segmentation

Multi-modality MRI dataset for brain tumor segmentation

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,...

The dataset contains 293 HGG and 76 LGG pre-operative scans in four MRI modalities, which are T1, T2, T1c and FLAIR.

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...