19 datasets found

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

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,...

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.

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 TCIA-Glioma dataset is a multi-modal MRI scans dataset for brain tumor segmentation.

The rsna-asnr-miccai brats 2021 benchmark dataset is a multi-modal MRI scans dataset for brain tumor segmentation.

Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. In the realm of clinical screening and...

The BraTS2021 dataset is an important resource in the field of brain tumor segmentation, consisting of multi-modal magnetic resonance imaging (MRI) data, including four imaging...

Multimodal Brain Tumor Image Segmentation Challenge (BraTS) 2018 dataset used for brain tumor segmentation and overall survival prediction.

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

Brain Tumor Segmentation dataset contains images of brain tumors from MRI scans. The dataset is used for automatic segmentation of brain tumors.

The Brain Tumor Segmentation (BraTS) 2019 dataset provides 335 training subjects, 125 validation subjects and 167 testing ones, each with four MRI modality sequences (T1, T1ce,...