35 datasets found

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...

Automated segmentation of 3D brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.

rs-fMRI data from 56 brain tumor patients who underwent preoperative mapping at our institution.

The dataset used in this study for prognostic stratification of glioblastoma patients based on computational AI-based algorithms. The dataset consists of whole slide images (WSI)...

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

Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly...

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

Brain tumor MRI dataset used for classification of brain tumors into Meningioma, Glioma, and Pituitary tumor

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 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,...

Brain tumour MRI segmentation dataset

Brain tumor segmentation dataset

Brain tumor segmentation dataset

Brain tumor segmentation dataset

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.

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).