144 datasets found

The dataset used for self-supervised learning of 3D medical images.

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging...

Compressed Sensing MRI reconstruction using a Generative Adversarial Network with a Cyclic Loss

Compressed Sensing MRI reconstruction using a Generative Adversarial Network with a Cyclic Loss

A dataset of 2857 heart short-axis MRI slices of 292 patients with hypertrophic cardiomyopathy, used for training and evaluation of deep learning models for left ventricular...

Brain tumor MRI dataset used for classification of brain tumors into Meningioma, Glioma, and Pituitary tumor

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...

The dataset used for vestibular schwannoma MRI segmentation

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

The Anatomical Tracings of Lesions After Stroke (ATLAS) dataset is a collection of 229 T1-weighted normalized 3D MR images with diverse lesions manually segmented by specialists.

A dataset of mouse brain MRI images for automatic skull stripping and segmentation using U-Net architecture.

A large preclinical stroke imaging study with multi-modal MRI datasets, including quantitative multi-echo T2 and apparent diffusion coefficient (ADC) maps.

The 0.36T MRI dataset was acquired at the University College Hospital, Ibadan, Nigeria.

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,...

A dataset used for training and testing machine learning models for cerebral microbleeds detection.

The IXI dataset is a collection of brain development images

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.