10 datasets found

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

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.

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 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.