20 datasets found

The dataset used for training and testing the U-Net CNN model.

The dataset used for automatic segmentation of the human claustrum using U-Net CNN model.

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms, focusing on stroke lesions in brain MRI images.

The dataset used for training and testing the deep learning model for detecting enlarged perivascular spaces (ePVS) on brain MRI.

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

The dataset used in the paper is a collection of 1262 t1-weighted brain MRIs of subjects from three different datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1),...

The dataset used for training and testing the DeepMediX model, which consists of brain MRI and skin cancer images.

The dataset consists of T1-weighted brain MRI images of adults, with manual segmentation of anatomical structures provided.

The MSLUB dataset consists of brain MRI scans of 30 patients with multiple sclerosis.

The Calgary-Campinas brain dataset consists of 45 fully sampled T1w volumes acquired from a 12-channel clinical MR scanner.

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 dataset used in this study for brain sMRI analysis, which includes seven public datasets, including one dataset for pre-training and other six datasets across three diseases...

A dataset of brain MRI images for evaluation of segmentation algorithms.

A dataset of brain MRI images for evaluation of semi-supervised anomaly segmentation algorithms.

The HCP30 dataset used for the Medical Out-of-Distribution (MOOD) challenge

The LPBA40 dataset contains 3D brain MRI scans of 40 healthy adults, with image dimensions of 256×124×256 and a voxel size of 0.8938×1.500×0.8594 mm³. Each scan is precisely...

The dataset consists of T1-weighted brain MRI images of patients, with manual segmentation of anatomical structures provided.

The dataset used for testing was chosen based on their popularity for evaluating registration and variation in structure parcellation methods.

The dataset used for brain tissue segmentation in MRI images

The CANDI dataset comprises 54 T1-weighted brain MRI scans and anatomical segmentations.