14 datasets found

The BraTS 2017 dataset, used for training and testing the segmentation models.

The 2D brain tumour dataset proposed in [16], created from axial slices extracted from the MRI volumes of the 3D brain tumor segmentation challenge (BraTS) 2017 data.

A medical cross-modality domain adaptation benchmark proposed in [6] to validate the performance of our proposed unsupervised cross-modality domain adaptation method for...

The dataset used for vestibular schwannoma MRI segmentation

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

Bladder tumor dataset contains 2200 MRI slices from 25 patients with pathologically confirmed bladder cancer.

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,...

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 V-Net is a deep learning model for medical image segmentation that uses a U-Net architecture.

Medical image segmentation dataset

The TCGA_LGG database is a repository of brain images for the study of lower grade gliomas.

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.