11 datasets found

The Medical Segmentation Decathlon (MSD) is a collection of 10 benchmark datasets for segmentation spanning different body parts and modalities.

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

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...

Segmentation of retinal OCT B-scans into 7 retinal layers and accumulated fluid.

Abdominal organ segmentation in 20 test ceCT scans of the Visceral dataset.

Three challenging medical applications: whole-brain, whole-body and retinal layer segmentation.

The dataset contains 3D thoracic CT scans for segmentation of thoracic organs.

The NIH dataset contains 14 different diseases, including pneumonia, and is used for training and testing machine learning models.

The dataset used in this paper for medical image segmentation, including images from the ISBI-2012 dataset.

This dataset contains abdominal CT and MRI images from 40 patients.

LIDC-IDRI dataset contains almost all the related information for low-dose lung CTs including several doctors' annotations on nodule sizes, locations, diagnosis results, nodule...