20 datasets found

A dataset of 10,520 brain MRI scans acquired at Massachusetts General Hospital, used for evaluation of the proposed segmentation method.

A dataset of more than 10,000 uncurated, heterogeneous brain MRI scans acquired at Massachusetts General Hospital.

The dataset used for evaluation of the proposed method for detecting enlarged perivascular spaces in brain MRI.

The dataset used in this paper is 3D T1-weighted brain MRI scans from the multi-site ABIDE-1 study.

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

The dataset contains 2000 brain MRI scans scored visually by a single expert rater.

The Rotterdam Scan Study is a population-based study investigating neurological diseases in the middle-aged and elderly. The dataset contains 2017 3D MRI scans from 3 different...

Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training 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.

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.

Modeling the behavior of coupled networks is challenging due to their intricate dynamics. For example in neuroscience, it is of critical importance to understand the...

The probabilistic atlas dataset, which includes MRI scans of 20 subjects.

The dataset used for the proposed Segmentation Auto-Encoder (SAE) method for adaptive image segmentation.